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THE DETERMINATION OF HEATING VALUES OF FUELS

The Count of Monte Cristo, Illustrated: Chapter 5 - The Marriage Feast

Can you name them by just looking at their faces? You should be able to given their accomplishments. The twentieth century is not so long ago, but time is weird and history weirder. When you are living history in the way we are today it is difficult to find perspective. Everything seems so very new, but the human condition...it doesn't change as much. If you read about the Roaring Twenties, there was so much excitement during the time period. There are huge technological leaps, but also some of the greatest social change of the 20th century. There is so much optimism and forward-looking energy at the beginning of the 20th century. Every single American in the Roaring Twenties is looking to a future of great promise. The nineteenth century was in everyone’s rearview mirror. The horse and buggy era was long gone. There was nothing to be learned from that time when we were driving cars and flying in airplanes. I detect a similar zeitgeist today. There is this belief in technology. A strong belief it can solve ALL our problems if it is just properly applied. It was similar in the 20th century though. Technology did NOT prevent the Great Depression or World War II. We learned about our human flaws again...as we often do. In these times of strife, I am a little surprised that these three women get so little attention. However, like of the nineteenth century perhaps they are just seen as dinosaurs. Trapped in their times with nothing to offer the future. I fear we are about to learn about the extent of our human flaws again... Harriet Tubman Despite that fear, there remains hope for a country which produces people like the three who follow. Three Americans from the 20th century who ought to be celebrated in the 21st century. They are trailblazers. They broke ground for all of us. A consideration of their lives may help us reduce some of our human flaws. 1. Bessie Coleman I will hazard a guess that few readers have heard of this woman. She died tragically young, but she was a real trailblazer. Chauvinists of the era might believe women could not operate mechanical things. Similarly, racists thought the indigenous peoples of North America AND Africa were similarly handicapped, if not more so. Yet Bessie Coleman was the first African-American woman and first Native-American to hold a pilot license. She earned her pilot license from the Fédération Aéronautique Internationale. These are strong words. They are insightful words. Her tragic early death seems unfair. You see she was killed while her mechanic/agent/publicist was flying the damn plane. She was getting ready for a parachute jump and the dumbbell crashed the plane killing her and himself. She died in 1926 at quite a young age. It is probably one of the reasons why she is not remembered. Still, to have died because a white male Texan had failed to secure his tools in the cockpit... Subsequently, his wrench got lodged in the controls during flight causing the fatal crash. It seems a really stupid way to check out and a terrible irony for chauvinists and racists alike. Killed by dumb white male arrogance was certainly a legitimate interpretation of how she passed. It is tragic and sad. 2. Moms Mabley When I was a child, Occasionally, I would see her when the adults would let me stay up late while they drank coffee and gossiped or whatever adults do while they ignore children. Goodness, she was quite funny. She also had a non-threatening grandmotherly air about her. Late in her life though, the fight for civil rights really heated up. Though Moms Mabley was near the end of her career, she was able to get traction with young people. this woman was making the late-night comedy circuit. She even made jokes about being a lesbian during , which had to be some kind of first somewhere/somehow in the LBGTQ community. I did not get the jokes at first, I was young. Eventually, I understood, but I wonder about the adults who were in the same room with me at the same time. her stand-up As I grew older, she appeared in more serious venues too. I saw her sing some songs. I saw her give some interviews. I learned who Moms Mabley really was. She had had quite a hard life. She had been raped twice in her childhood, once by a family member and then by a white sheriff. Both these incidents led to pregnancies which she brought to term and then gave the baby up for adoption. It was then at the urging of her grandmother that she ran off at the age of 14 to join the vaudeville circuit. As tough as that must have been be on the road at 14, it could not have been as tough as what she was fleeing. To learn of these things made me very sad. I cried a tear for Moms Mabley. This woman’s life is so trailblazing in SOOO many ways, I am shocked she does not get more attention. I provided you with a link to some of her stand-up above, but she was so much more than that. You have to see her in a more relaxed setting to really appreciate what she was bringing to the table. In fact, even if you don't read the rest of this you really must watch this video of her rendition of Abraham, Martin and John. Here she is with Sammy Davis, Jr. 3. Shirley Chisholm I really had to save this woman for last. America needs someone like Shirley Chisholm right now. During the sixties and seventies, she was right there in my face of Establishment politics. She could articulate her political opinions with great flourish. When I hear Tyler Perry's Madea character enunciate certain words, I think of Shirley Chisholm. She had a similar way of speaking sometimes. She ran for president in 1972. There is no way to call it a symbolic campaign either. She put up numbers at the convention, which I watched as a child. Her largest support overall came from Ohio, with 23 delegates with a total of 152 first ballot votes at the Democratic convention. Her total gave her fourth place in the roll call tally, behind McGovern’s winning total of 1,728 delegates. Chisholm said she ran for the office . BADASS! “in spite of hopeless odds … to demonstrate the sheer will and refusal to accept the status quo” Goodness, we need women like this today. I cannot believe I do not see this woman’s old speeches all over social media. It is shocking to me given today’s social climate that the name Shirley Chisholm is not on every activist’s lips. If somehow and someway Shirley Chisholm could be resurrected, I feel she would be a shoe-in for the Presidency of the United States this time around. Read this again - " . I don't think a person like Shirley Chisholm would not face hopeless odds today. in spite of hopeless odds … to demonstrate the sheer will and refusal to accept the status quo” Unbought and unbossed, yeah!

3 Black Women of the 20th Century Who Deserve More Recognition

2018: A Review of the the First Year of the DApps: the Anecdotes You Know and Don’t Know (Part I)

Many have written about classification and categorization of crypto assets. Security tokens, utility tokens, cryptocurrencies, exchanges, appcoins, and altcoins are all labels in an effort to organize a collection of similar crypto assets. Some people, like Tom Lee (whom I respect very much) of , break the collections into cohorts based on how they trade — more like sectors. For example, Lee breaks the sectors into commodities, platforms, privacy, exchanges and stablecoins. He does this because these cohorts trade similarly, and he can based on their collective attributes. Sector definition is important, but I think there’s one higher level of abstraction needed. That’s crypto asset classes. Commodities, platforms and stablecoins are of a different classification type than privacy coins and exchanges, and I would separate them as such. That way, we’ll have both asset classes and sectors like we do with traditional financial markets. Fundstrat Global Advisors model trading action Others, like the general partners at , break the collections into 3 major cohorts: currencies (stores of value); security tokens (tokens backed by real-world assets); and utility tokens (work tokens). They’ve settled on this configuration because they think each has a separate and distinct valuation model. They outline a for currencies and work tokens. You’ll notice that they don’t distinguish platform coins from cryptocurrencies as they believe platform coins will be held for their own specific store of value. Multicoin Capital valuation model Some labels have just come to us through nomenclature “inheritance” which I don’t think is very helpful. An example of this is the moniker “altcoins”. It can be confusing as to exactly what an altcoin is and whether it can mean every other coin that is not bitcoin or ether. That’s a large subset that really doesn’t do anyone much good. I think of the crypto asset class superset a little differently. I would classify the mutually-exclusive subsets based on logical groupings. I see and eight distinct crypto asset classes — reserve, currencies, platforms, utility tokens, security tokens, commodities, appcoins stablecoins. I use these crypto asset classes because members of the set have similar properties. They have similar functional goals and react to the market in a similar fashion. They have similar valuation models, compete with one another in some form or operate similarly within their set, have similar regulatory risk and generally define the highest subset under the umbrella crypto asset superset. Fundamentally, I’m coming at this as an investor. I want to have an asset allocation strategy, and then have multiple positions of the best coins I think will produce a return for that crypto asset class. I want to be able to manage target asset class percentages. I don’t want to commingle currencies, utility tokens, platforms and appcoins in the same asset class because it does not provide me with a satisfactory level of precision to allocate across the total market. The asset classes may have similar valuation models, but their stated goal and their functions are different. From an investor’s perspective, it makes sense to create 6–12 classes that are mutually exclusive while also being exhaustively comprehensive to classify members in the entire crypto asset superset. Reserve Assets The reserve crypto asset class contains just two coins — bitcoin and ether. I’m pulling Ethereum out of the platforms crypto asset class for now because it has special properties. Ultimately, investors need to convert their fiat currency into either bitcoin or ether to buy any other coin or token. These act as reserve crypto assets in the market. While bitcoin and ether are different in many ways, they both have a market quality similar to one another and not shared by any other crypto assets. They are both mined and have set monetary policies. Cogent arguments have been made that bitcoin is its own distinct crypto asset class because its deflationary cryptocurrency with limited supply. The Ethereum network is a platform with different supply/demand mechanics than Bitcoin. However, as long as these two crypto assets hold this special dynamic within the market, I’m going to consider them both reserve assets. Let’s take a page from the financial analog. The entire current global asset structure starts with gold. Gold is the first form of money because it is not issued or controlled by a central authority. Moreover, it does not have counter-party risk. We have agreed on its price, based on its scarcity, which has held for millennia. From gold, countries manage their fiat money supplies through their central banks. In the past, the amount of gold a country owned affected how much of its currency supply could be printed. From the formation of currency, a government creates bonds which include an interest rate. Moreover, commodities are priced and other fixed income (bonds) are created to produce a return on capital in the financial market. Equity can be created as a security, after pricing resources (commodities) and calculating interest rates and risk. While gold is classified as a commodity (for legal and other reasons), it’s treated as both a commodity and a currency. It has special properties which convince asset allocators and investors that gold is an asset class of its own. I agree. The same is true for the reserve crypto assets. decentralized Cryptocurrencies Cryptocurrencies’ core benefit to the market is to be a digital form of payment. The fundamental question to test whether or not a coin is a currency asks “Is the primary use as a medium of exchange and/or a store of value?” That is, does it digitally fulfill on a currency’s ability to be: a medium of exchange; a store of value; and, ultimately, a unit of account. While each cryptocurrency is at various stages of delivering on these, that is the primary goal or function. Cryptocurrencies collectively would have similar regulatory risk as a collection compared against other crypto asset classes, i.e., utility tokens, depending on how they came to market. Cryptocurrency has a similar valuation function as standard currency. As such, the Equation of Exchange, M x V= P x Q, is applicable here. The valuation model of cryptocurrency is fundamentally based on unit expenditure and, more importantly, on the velocity of money. As money is used faster throughout the system, the value of that monetary system increases. Cryptocurrencies are all trying to address the same issues with payment. Cryptocurrencies are grouped logically, and there is no superset between this asset class and the crypto asset umbrella superset. Therefore cryptocurrency is its own crypto asset class. Examples of cryptocurrencies are: Bitcoin, Bitcoin Cash, Monero and Dash. Platforms Platforms are going to be one of the most interesting cryptos because this is where smart contracts and programmatic money are going to exist. If a blockchain wants to implement smart contracts in some form, then it’s probably in the platform asset class. Programmatic enforcement of legal and financial contracts will transform the shape and velocity of what’s possible globally. We’re already seeing a fundamental change in what’s possible with the accumulation, transference and store of value. To understand what’s possible, let’s paint a picture of the future in 20 years. All corporations will effectively be smart contracts. Governance, voting, supply chain and contract enforcement will fundamentally change because we are going to be able to programmatically store and transfer value based on some previously agreed-upon logic. We will be able to see exactly where our food comes from and how its handled along the way. We will be able to trust digital voting has not been tampered with because of the immutability of a blockchain. Land title disputes will no longer exist. Value will be created globally just from the transparency and predictability of contract execution. Platforms will work to provide some of the fundamental building blocks of blockchain, like smart contracts, immutability and decentralization. However, they are distinct from cryptocurrency in their stated goal and function. They have different regulatory risk than other crypto asset classes, depending on how they were created (e.g. based on regulatory region and whether a public ICO or private pre-sale governed sale, etc.) Platforms are closer to legal contracts than money in the analog paradigm. Their valuation model is primarily based on , which is described by — the value of the network is proportional to n² users of the network. Whereas, cryptocurrencies are valued on the velocity of money and the equation of exchange. Platforms are distinct from all other crypto asset classes, and thus they are an asset class of their own. network effects Metcalfe’s Law Examples of Platforms are: EOS, NEO, Cardano & IOTA. Utility Tokens Utility tokens focus on a providing a service layer. They provide a function or a resource to address a specific need. They have partners and contributors who create applications on top of the protocol or create applications that use the utility token. Utility tokens valuation model is primarily based on network effects and are influenced by supply/demand. Utility tokens run on a platform blockchain they do not directly manage or control. Most utility tokens to date run on the Ethereum platform. Utility tokens are distinct from platforms in that they aren’t providing a lower level or horizontal functionality built on top of a blockchain like a protocol and they are distinct from appcoins because their stated functional goal is not limited to one application or service. Utility tokens may work on one blockchain for now, but in the future the utility tokens may operate on multiple networks to serve multiple appcoins. Examples of Utility Tokens are: BAT, Civic, OmiseGO and 0x. Photo by on Igor Ovsyannykov Unsplash Security Tokens Security tokens are different from utility tokens and platforms by their functional nature and goal as well as by their regulatory risk profile. They derive their value from being linked to an external asset and are likely subject to federal securities regulations here in the U.S. The valuation model of a security token directly relates to valuing the underlying asset linked to the token. These tokens will have a much bigger play and their growth stage in 2018 and beyond. I predict within the next 12 months there will be companies that want to have their equity trade as security tokens on a crypto exchange. We’ll also see real estate secured on a blockchain with a security token. Security tokens are distinct from all other crypto asset classes because of their link to an external, real-world asset. An example of a Security Tokens is: BCAP. Crypto Commodities Commodities aim to provide a direct consumable resource. This is distinct from the platform asset class in that it’s vertical/industry specific and it seeks to exchange based on a specific subset of resources. Those resources could include disk space, computing power or other consumable services. Commodities are distinct from utility tokens in that their valuation model is mostly influenced by the supply and demand of a given resource. Crypto-commodities run their own blockchain which is different than how utility tokens operate. This is one of the main mechanics to how supply and demand affect price. Price surges and other scarcity risks could occur more frequently with commodities than may affect other asset classes like utility tokens or appcoins. Though, they will have less regulatory risk because of the work or resource exchanged, assuming appropriate measures were followed in their pre-sale. Their valuation model is influenced by network effects but the valuation model is different than currencies which are mostly valued by the velocity of money and platforms which are valued mostly by network effects. Examples of crypto-commodities are: STORJ, FileCoin and Golem. Appcoins / dApps Appcoins work on one specific network to provide a specific vertical application service. They are distinct from commodities in that their supply is not constrained like a commodity or resource that is consumed. They are different from platforms because they are vertical and application-specific. They aren’t providing a platform or protocol for others to build upon. Their goal and utility is distinct in focusing on their own narrow ecosystem. They have a many-to-one relationship in their network model whereas utility tokens have a many-to-many relationship. Appcoins are the most narrow applications and therefore levered to a specific use case as being a “pure play” to a particular application or service. As a result, their valuation model will be distinct from commodities, currencies or platforms. Examples of Appcoins are: Steem, Binance and SALT. Stablecoins Stablecoins are present to provide a stable store of value. Currently, there is one major player within this crypto asset class. That is Tether. However, that will change in the future. Currently there are several projects pursuing various methods on how they will achieve a stable coin. I recommend this to learn more about stablecoins. This is a new and burgeoning asset class, and we will distinguish it on its own due to its stated goal and functional nature. article from Multicoin Capital Examples of Stablecoins: Tether, Maker, Basecoin & Digix Conclusion Hopefully, this makes a case for a good classification model of crypto asset classes. This could change as we’re in the very early stages of this new asset class. It’s possible appcoins could merge into utility tokens or crypto-commodities merge into platforms. My goal is to help investors build a portfolio with appropriate asset allocation to improve your risk-adjusted returns. To achieve that, we need a good framework for how to think about crypto asset classes. If Modern Portfolio Theory (MPT) suggests a return can be made only 3 ways — portfolio construction, asset selection and market timing — then we want a structure to help guide and enforce how we think about asset allocation. A comprehensive crypto asset class model will help in this effort. I’ll leave selection and market timing to other upcoming articles. Happy Hunting! Jake Disclaimer: The above references an opinion and is for information purposes only. It is not intended to be investment advice. Please do your own homework. Jake Ryan is the founder of Wealth Rituals and Tradecraft Capital, a startup advisor, an angel investor & writer on investing. If you enjoyed this article “clap” to help others find it! For more, join us on Facebook , Twitter . #crypto #cryptocurrency #bitcoin #investing #howto #blockchain

Crypto Asset Classes

seems to have a effect on people – either they love it or hate it. It’s true that rebasing opens up a whole range of new and exciting ways to screw yourself over. Even better than that it offers up a range of wonderful opportunities to screw over your team mates as well. Yet despite that I am most definitely in the pro-rebase camp. After all, all the most powerful tools will burn you if you don’t use them properly. Having developed a fairly extensive set of rebase scars over the years here are my handy tips for dodging the pitfalls and making the most of rebase. Git rebasing marmite This assume that you are already familiar with the basic operations of . If not the is probably a good place to start. git rebase git rebase documentation Always rebase interactively Git has two main forms of rebase – interactive and non-interactive. Essentially they do the same thing – take your branch and reapply the code changes on top of another branch. Interactive rebasing offers a bunch more options such as re-ordering and re-wording commits but for the safety-conscious rebaser, the main thing it offers is that it shows you a list of commits that will be re-applied before the rebase starts. In my experience, the vast majority of rebase disasters can be averted by simply reviewing this list and aborting the rebase before it even starts. If the list is not what you expect then run away, figure out why and then try again. Example interactive rebase commit list Be conscious of how you bring in upstream changes A corollary of always rebasing interactively is that you should never use . I would go a step further and say that the only version of git pull you should use is . This is because both the rebase and the default case may result in the code you are working on changing without you realising it. They will both attempt to transparently incorporate any changes made upstream without advising you what they are or letting you review them. A classic case of where this is undesirable is when you and a colleague both add the same method to a class but in a different place in the file. Git will quite happily combine these changes resulting in a duplicate method definition. Maybe you or your colleague will catch it in code review but it is better to avoid it in the first place by being mindful of when the code you are basing your changes on may have changed. git pull --rebase git pull --ff-only Go slow and keep it simple It’s very easy to get excited when you start an interactive rebase. But instead of just diving in and throwing commits here, there and everywhere, taking it easy and spreading the rebase over a few steps will save you a lot of pain and misery in the long run. The first thing I do is an interactive rebase without changing anything. This way you can review the commits and deal with any upstream conflicts up front without having to try and figure out whether the conflicts are a result of something that has changed in the branch you are rebasing onto or whether it is a problem you have created with all your commit juggling. Then as you proceed keep moving, squashing and editing commits in small chunks. That way if you do get any unexpected conflicts you can abort the rebase and figure out the problem without losing any other changes you may have made. Nothing’s worse than going through a marathon rebase to get within sight of the end and having to abort the whole thing because you’ve swapped the wrong two commits around and now one of them won’t apply. Don’t be afraid to run away Rebasing shouldn’t be hard. If it is there is probably an underlying problem with what you are trying to do. Perhaps something has changed upstream that you weren’t aware of. Perhaps you have files in a commit that you weren’t expecting to be there. Don’t be afraid to abort the rebase so that you can stop and rethink what you are trying to do without the pressure of being in rebase limbo. The best way to resolve conflicts is not to have any It is inevitable that you will sometimes run into conflicts caused by changes your colleagues have made. One way or another you will have to deal with them, whether it is via rebasing, when you merge or by shouting at your colleagues until they revert their changes so that you can merge first and thereby force them to deal with the conflicts. Conflicts caused by moving commits around within your own branch are more avoidable however. Keep your commits small and focused. The fewer files and changes in a commit, the less likely it is to conflict and so the easier it is to reorder. If you are TDDing then a good place to start is committing each time you add a test and get the associated code passing. That way you are only ever committing two files at a time and each file will contain a small change. Sometimes more is less The more here being the number of commits and the less being the amount of pain involved in rebasing. If you have ignored the previous suggestion of committing often then that doesn’t mean all is lost. Interactive rebasing allows you to edit a commit. If you have added too many files to a commit and so now can’t move it around, you can edit the commit, remove some of the changes from the commit and create a new commit for them. This sounds complicated but in fact is as simple as marking the commit for editing in the interactive rebase commit list. Then when the rebase pauses run git reset HEAD~ some/file/for/another/commitgit commit --amendgit add .git commit -m '- message for the separate commit'git rebase --continue This will leave all the changes to that file in a separate commit. Another variation on this is for when you have multiple changes to the same file that have accidentally ended up in the same commit. In this case is your friend. This will allow you to choose which of the changes to the file are merged back into your original commit and which stay unstaged ready to add to another commit. git add -p . Know how to fix it when it all goes wrong One of the beauties of git is that it is actually quite hard to completely lose anything once it has been committed. If you do get to the end of your rebase and realise you’ve made a pig’s ear of it, your previous commits still exist and you can easily revert back. First you need the SHA of the tip of your branch before the rebase. Assuming you don’t have this to hand, you can find it using . You will be able to see where your rebase started easily enough as the line will contain . You just need to find the SHA of the commit before this and reset to that commit with . git reflog rebase -i (start) git reset --hard SHA Force push if you have to, but make sure you really need to first One of the oft-cited reasons for avoiding rebasing is that in order to push your changes back upstream you will need to force push, with the potential to overwrite other user’s changes. This is of course completely possible and a great way to antagonise your colleagues. However just because you can screw things up by force pushing doesn’t mean you have to. First of all, you only use rebasing before you merge. Once you have merged something into one of your “main” branches (typically master but perhaps you have other special release branches) then it’s there for good. If you find a problem afterwards you have to live with it or add another commit. You can rebase other branches that you share with colleagues if you choose to. You simply need to communicate as to who is doing what. We do this on a daily basis without issue. It helps if people are responsive. So if one of my colleagues tells me that they have force pushed a branch we are sharing then I will update my local copy straight-away. It helps avoid conflicts and trying to figure out whether a force push should or shouldn’t be necessary. You should also know before you attempt to push whether you are expecting a force push to be necessary. If you get the classic force push error message (along the lines of ) when you weren’t expecting it then don’t just automatically force push. Stop and see what commits you have locally compared to the remote version and then it should become clear what you need to do to fix it. error: failed to push some refs... My personal rule of thumb around shared branches is that if a colleague and I are working on the same feature at the same time, I will create my own branch from the feature branch, work on that and then rebase and merge back into the shared feature branch. It is the safest option and since you both may be pushing frequently it saves constant back and forth. If we are working independently in more of a handover type scenario then we will probably both just work on the same branch as the chances of treading on each other’s toes is much smaller. The other approach, which is probably what we do most commonly, is save the rebase until the end. So we both just keep adding commits to the tip of the branch and then one or both of us will perform the rebase tidy up once we are happy that the feature is ready to go. Use the correct type of merge The aim of rebasing is not to avoid merge commits or maintain a linear history. Merge commits can be very helpful in identifying a set of commits that have some degree of cohesion. However at the other extreme where every single commit has a matching merge commit they are unhelpful and clutter up your log. The answer is simply to always merge with the correct flag set. If I am merging a single commit or if the branch contains multiple unrelated commits then I will do a . If not I will do a . Simple as that. git merge --ff-only git merge --no-ff You don’t have to wait til the end to rebase There are definite benefits to rebasing your code once you have completed all the tasks as discussed above and in . However sometimes you will add a commit just to fix a typo in an earlier commit or something equally trivial. It can be beneficial to just squash these changes straight into the correct commit immediately in order to reduce the number of commits you are working with in your final pre-PR rebase. The poetry of pull requests So there we have it, 10 handy tips for smooth and efficient rebasing. However knowing techniques for juggling commits is only part of the picture. You also need to know what kind of end result you are looking to achieve. For more on that, check out . The poetry of pull requests

How to rebase safely for profit and pleasure

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
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Issue Discussion on Twitter. Online Information Review 40, 5 (2016), 660–672.
https://doi.org/10.1108/OIR-10-2015-0338 [10] Heather K. Evans and Jennifer Hayes Clark. 2016. You Tweet Like a Girl!’: How Female Candidates Campaign on Twitter. American Politics Research 44, 2 (2016), 326–352. https://doi.org/10.1177/1532673X15597747 [11] Peter A. Gloor, Joao Marcos, Patrick M. de Boer, Hauke Fuehres, Wei Lo, and Keiichi Nemoto. 2015. Cultural Anthropology through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-based Sentiment. arXiv:1508.00055 [cs.CY] https://doi.org/10.48550/ARXIV.1508.00055 [12] Leonie Huddy and Nayda Terkildsen. 1993. Gender Stereotypes and the Perception of Male and Female Candidates. American Journal of Political Science 37, 1 (1993), 119. https://doi.org/10.2307/2111526 [13] Berrin Beasley John H. Parmelee, Nataliya Roman and Stephynie C. Perkins. 2019. Gender and Generational Differences in Political Reporters’ Interactivity on Twitter. Journalism Studies 20, 2 (2019), 232–247. https://doi.org/10.1080/1461670X.2017.1364140 [14] Ashiqur R. KhudaBukhsh Kunal Khadilkar and Tom M. Mitchell. 2022. Gender Bias, Social Bias, and Representation in Bollywood and Hollywood. Patterns 3, 4 (2022), 100486. https://doi.org/10.1016/j.patter.2022.100486 [15] MingYu (Ethen) Liu. 2023. Topic Modeling. https://ethen8181.github.io/machine-learning/clustering/topic_model/LDA.html. [16] J. Nathan Matias, Sarah Szalavitz, and Ethan Zuckerman. 2017. FollowBias: Supporting Behavior Change toward Gender Equality by Networked Gatekeepers on Social Media (CSCW ’17). 1082–1095. https://doi.org/10.1145/2998181.2998287 [17] Lindsey Meeks. 2016. Gendered Styles, Gendered Differences: Candidates’ Use of Personalization and Interactivity on Twitter. Journal of Information Technology & Politics 13, 4 (2016), 295–310. https://doi.org/10.1080/19331681.2016.1160268 [18] Armin Mertens, Franziska Pradel, Ayjeren Rozyjumayeva, and Jens Wäckerle. 2019. As the Tweet, so the Reply? Gender Bias in Digital Communication with Politicians. In Proceedings of the 10th ACM Conference on Web Science (WebSci ’19). 193–201. [19] Sebo P. 2021. Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference. J Med Libr Assoc. (2021). [20] Anmol Panda, A’ndre Gonawela, Sreangsu Acharyya, Dibyendu Mishra, Mugdha Mohapatra, Ramgopal Chandrasekaran, and Joyojeet Pal. 2020. NivaDuck - A Scalable Pipeline to Build a Database of Political Twitter Handles for India and the United States. In International Conference on Social Media and Society. 200–209. [21] John H. Parmelee. 2013. Political Journalists and Twitter: Influences on Norms and Practices. Journal of Media Practice 14, 4 (2013), 291–305. https://doi.org/10.1386/jmpr.14.4.291_1 [22] Pallavi Prasad. 2020. One in Every Seven Tweets About Indian Women Politicians Is Abusive: Study. https://www.theswaddle.com/women-politicians-india-twitter-trolls. [23] Gazal Shekhawat, Parantak Singh, Rynaa Grover, Saloni Dash, Himani Negi, Ankur Sharma, Somya Sagarika, and Joyojeet Pal. 2021. Twitter Use by Politicians Across States in India. [24] Carole Spary. 2007. Female Political Leadership in India. Commonwealth & Comparative Politics 45, 3 (2007), 253–277. https://doi.org/10.1080/14662040701516821 [25] Genderize team. 2023. Genderize. https://genderize.io/. [26] Jesse Holcomb Usher, Nikki and Justin Littman. 2018. Twitter Makes It Worse: Political Journalists, Gendered Echo Chambers, and the Amplification of Gender Bias. The International Journal of Press/Politics 23, 3 (2018), 324–344. https://doi.org/10.1177/1940161218781254 [27] Jason Gainous Wagner, Kevin M. and Mirya R. Holman. 2017. I Am Woman, Hear Me Tweet! Gender Differences in Twitter Use among Congressional Candidates. Journal of Women, Politics & Policy 38, 4 (2017), 430–455. https://doi.org/10.1080/1554477X.2016.1268871 This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References REFERENCES [1] Toril Aalberg and Anders Todal Jenssen. 2017. Gender Stereotyping of Political Candidates. Nordicom Review 28, 1 (2017), 17–32. https://doi.org/10.1515/nor-2017-0198 [2] Pushkal Agarwal, Nishanth Sastry, and Edward Wood. 2019. Tweeting MPs: Digital Engagement between Citizens and Members of Parliament in the UK. Proceedings of the International AAAI Conference on Web and Social Media 13, 01. [3] Arshia Arya, Soham De, Dibyendu Mishra, Gazal Shekhawat, Ankur Sharma, Anmol Panda, Faisal M Lalani, Parantak Singh, Ramaravind Kommiya Mothilal, Rynaa Grover, Sachita Nishal, Saloni Dash, Shehla Rashid Shora, Syeda Zainab Akbar, and Joyojeet Pal. 2022. DISMISS: Database of Indian Social Media Influencers on Twitter. [4] Nichole M. Bauer. 2015. Emotional, Sensitive, and Unfit for Office? Gender Stereotype Activation and Support Female Candidates. Political Psychology 36, 6 (2015), 691–708. [5] Aditi Bhowmick, Paul Novosad, Sam Asher, Elliott Ash, Bilal Siddiqi, Christoph Goessmann, Daniel Chen, and Tanaya Devi. 2021. In-Group Bias in the Indian Judiciary: Evidence from 5.5 Million Criminal Cases. In Proceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ’21). 47. https://doi.org/10.1145/3460112.3471943 https://doi.org/10.1145/3460112.3471943 [6] Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa-Anke, Fangyu Liu, Eugenio Martínez-Cámara, et al. 2022. TweetNLP: Cutting-Edge Natural Language Processing for Social Media. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. [7] Tessa M. Ditonto, Allison J. Hamilton, and David P. Redlawsk. 2014. Gender Stereotypes, Information Search, and Voting Behavior in Political Campaigns. Political Behavior 36, 2 (2014), 335–358. https://doi.org/10.1007/s11109-013-9232-6 https://doi.org/10.1007/s11109-013-9232-6 [8] Kathleen Dolan and Timothy Lynch. 2014. It Takes a Survey: Understanding Gender Stereotypes, Abstract Attitudes, and Voting for Women Candidates. American Politics Research 42, 4 (2014), 656–576. https://doi.org/10.1177/1532673X13503034 https://doi.org/10.1177/1532673X13503034 [9] Heather Evans. 2016. Do Women Only Talk about ‘Female Issues’? Gender and
Issue Discussion on Twitter. Online Information Review 40, 5 (2016), 660–672. https://doi.org/10.1108/OIR-10-2015-0338 https://doi.org/10.1108/OIR-10-2015-0338 [10] Heather K. Evans and Jennifer Hayes Clark. 2016. You Tweet Like a Girl!’: How Female Candidates Campaign on Twitter. American Politics Research 44, 2 (2016), 326–352. https://doi.org/10.1177/1532673X15597747 https://doi.org/10.1177/1532673X15597747 [11] Peter A. Gloor, Joao Marcos, Patrick M. de Boer, Hauke Fuehres, Wei Lo, and Keiichi Nemoto. 2015. Cultural Anthropology through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-based Sentiment. arXiv:1508.00055 [ cs.CY ] https://doi.org/10.48550/ARXIV.1508.00055 cs.CY https://doi.org/10.48550/ARXIV.1508.00055 [12] Leonie Huddy and Nayda Terkildsen. 1993. Gender Stereotypes and the Perception of Male and Female Candidates. American Journal of Political Science 37, 1 (1993), 119. https://doi.org/10.2307/2111526 https://doi.org/10.2307/2111526 [13] Berrin Beasley John H. Parmelee, Nataliya Roman and Stephynie C. Perkins. 2019. Gender and Generational Differences in Political Reporters’ Interactivity on Twitter. Journalism Studies 20, 2 (2019), 232–247. https://doi.org/10.1080/1461670X.2017.1364140 https://doi.org/10.1080/1461670X.2017.1364140 [14] Ashiqur R. KhudaBukhsh Kunal Khadilkar and Tom M. Mitchell. 2022. Gender Bias, Social Bias, and Representation in Bollywood and Hollywood. Patterns 3, 4 (2022), 100486. https://doi.org/10.1016/j.patter.2022.100486 https://doi.org/10.1016/j.patter.2022.100486 [15] MingYu (Ethen) Liu. 2023. Topic Modeling. https://ethen8181.github.io/machine-learning/clustering/topic_model/LDA.html . https://ethen8181.github.io/machine-learning/clustering/topic_model/LDA.html [16] J. Nathan Matias, Sarah Szalavitz, and Ethan Zuckerman. 2017. FollowBias: Supporting Behavior Change toward Gender Equality by Networked Gatekeepers on Social Media (CSCW ’17). 1082–1095. https://doi.org/10.1145/2998181.2998287 https://doi.org/10.1145/2998181.2998287 [17] Lindsey Meeks. 2016. Gendered Styles, Gendered Differences: Candidates’ Use of Personalization and Interactivity on Twitter. Journal of Information Technology & Politics 13, 4 (2016), 295–310. https://doi.org/10.1080/19331681.2016.1160268 https://doi.org/10.1080/19331681.2016.1160268 [18] Armin Mertens, Franziska Pradel, Ayjeren Rozyjumayeva, and Jens Wäckerle. 2019. As the Tweet, so the Reply? Gender Bias in Digital Communication with Politicians. In Proceedings of the 10th ACM Conference on Web Science (WebSci ’19). 193–201. [19] Sebo P. 2021. Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference. J Med Libr Assoc. (2021). genderize.io [20] Anmol Panda, A’ndre Gonawela, Sreangsu Acharyya, Dibyendu Mishra, Mugdha Mohapatra, Ramgopal Chandrasekaran, and Joyojeet Pal. 2020. NivaDuck - A Scalable Pipeline to Build a Database of Political Twitter Handles for India and the United States. In International Conference on Social Media and Society. 200–209. [21] John H. Parmelee. 2013. Political Journalists and Twitter: Influences on Norms and Practices. Journal of Media Practice 14, 4 (2013), 291–305. https://doi.org/10.1386/jmpr.14.4.291_1 https://doi.org/10.1386/jmpr.14.4.291_1 [22] Pallavi Prasad. 2020. One in Every Seven Tweets About Indian Women Politicians Is Abusive: Study. https://www.theswaddle.com/women-politicians-india-twitter-trolls . https://www.theswaddle.com/women-politicians-india-twitter-trolls [23] Gazal Shekhawat, Parantak Singh, Rynaa Grover, Saloni Dash, Himani Negi, Ankur Sharma, Somya Sagarika, and Joyojeet Pal. 2021. Twitter Use by Politicians Across States in India. [24] Carole Spary. 2007. Female Political Leadership in India. Commonwealth & Comparative Politics 45, 3 (2007), 253–277. https://doi.org/10.1080/14662040701516821 https://doi.org/10.1080/14662040701516821 [25] Genderize team. 2023. Genderize. https://genderize.io/ . https://genderize.io/ [26] Jesse Holcomb Usher, Nikki and Justin Littman. 2018. Twitter Makes It Worse: Political Journalists, Gendered Echo Chambers, and the Amplification of Gender Bias. The International Journal of Press/Politics 23, 3 (2018), 324–344. https://doi.org/10.1177/1940161218781254 https://doi.org/10.1177/1940161218781254 [27] Jason Gainous Wagner, Kevin M. and Mirya R. Holman. 2017. I Am Woman, Hear Me Tweet! Gender Differences in Twitter Use among Congressional Candidates. Journal of Women, Politics & Policy 38, 4 (2017), 430–455. https://doi.org/10.1080/1554477X.2016.1268871 https://doi.org/10.1080/1554477X.2016.1268871

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: References

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ ইন্টারঅ্যাকশনের মধ্যে জেন্ডার বায়াস উন্মোচন: রেফারেন্স

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Referenzen

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: referencias

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : références

भारतीय ट्विटर पर पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: संदर्भ

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：参考文献

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 참고자료

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: referências

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: ссылки

Hint Twitter'ında Gazeteci-Siyasetçi Etkileşiminde Cinsiyet Önyargısını Ortaya Çıkarmak: Referanslar

Khám phá sự thiên vị giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Tài liệu tham khảo

揭示印度推特上记者与政客互动中的性别偏见：参考文献

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 7. CONCLUDING DISCUSSION Misogyny remains an intractable social problem in India and the subcontinent. Subconscious gender bias manifests itself in many spheres of life and in several professions. This study seeks to uncover gender bias against female politicians on Twitter in India using NLP-based emotion analysis and LDA topic modeling- specifically in the interactions between journalists, who are the main drivers of political discourse on Twitter, and politicians. Our findings reveal a disconcerting trend: while popular journalists themselves do not seem to suffer from gender bias, on average, Twitter users display bias against female politicians. This bias is a reflection of the larger societal issue where male politicians traditionally occupy a higher pedestal in politics. This systematic bias is translating onto Twitter, and merely using balanced language is not enough to rectify the situation. To counteract this bias, we suggest utilizing recommender systems to amplify the voices of female politicians, as balanced language alone isn’t sufficient. Additionally, our analysis reveals that users tend to give more weight to the views of male journalists when evaluating female politicians, highlighting the need for diverse voices in political discourse. In summary, this study emphasizes the need to address gender bias on Twitter through not only language but also platform design and algorithms. Creating a more inclusive digital space is crucial to ensuring female public figures can effectively disseminate their views despite societal biases. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 7. CONCLUDING DISCUSSION Misogyny remains an intractable social problem in India and the subcontinent. Subconscious gender bias manifests itself in many spheres of life and in several professions. This study seeks to uncover gender bias against female politicians on Twitter in India using NLP-based emotion analysis and LDA topic modeling- specifically in the interactions between journalists, who are the main drivers of political discourse on Twitter, and politicians. Our findings reveal a disconcerting trend: while popular journalists themselves do not seem to suffer from gender bias, on average, Twitter users display bias against female politicians. This bias is a reflection of the larger societal issue where male politicians traditionally occupy a higher pedestal in politics. This systematic bias is translating onto Twitter, and merely using balanced language is not enough to rectify the situation. To counteract this bias, we suggest utilizing recommender systems to amplify the voices of female politicians, as balanced language alone isn’t sufficient. Additionally, our analysis reveals that users tend to give more weight to the views of male journalists when evaluating female politicians, highlighting the need for diverse voices in political discourse. In summary, this study emphasizes the need to address gender bias on Twitter through not only language but also platform design and algorithms. Creating a more inclusive digital space is crucial to ensuring female public figures can effectively disseminate their views despite societal biases.

Uncovering Gender Bias within Journalist-Politician Interaction: Concluding Discussion

সাংবাদিক-রাজনীতিবিদ মিথস্ক্রিয়া মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: আলোচনা সমাপ্তি

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern: Abschließende Diskussion

Descubriendo el sesgo de género en la interacción periodista-político: discusión final

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques : discussion finale

पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: समापन चर्चा

ジャーナリストと政治家の交流におけるジェンダーバイアスの解明：最終議論

언론인-정치인 상호작용에서 성 편견 발견: 토론 마무리

Descobrindo o preconceito de gênero na interação jornalista-político: discussão final

Выявление гендерных предубеждений во взаимодействии журналистов и политиков: заключительная дискуссия

Gazeteci-Siyasetçi Etkileşiminde Cinsiyet Önyargısını Ortaya Çıkarmak: Sonuç Tartışması

Khám phá sự thiên vị về giới trong tương tác giữa nhà báo và chính trị gia: Thảo luận kết luận

揭示记者与政客互动中的性别偏见：总结性讨论

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 6. LIMITATIONS This study focuses only on the top female/male journalists (based on their follower count) on Twitter. These journalists work for reputable media outlets which likely have strong guidelines and policies on gender. Therefore, our results underestimate the gender bias against politicians on Twitter. A more granular analysis of the tweets of lesser known journalists and their followers may reveal even higher levels of bias and misogyny. A similar assessment can be made on our choice of politician. We focus on the top 50 politicians of each gender in our analysis. These politicians are of considerable standing and thus less likely to face bias in their interactions with journalists. This may not be true in situations where journalists interact with female politicians of lower standing and political clout. Thus by focusing on the top fifty female politicians, we underestimate the bias that average female politicians may be subjected to on Twitter. Lastly, our analysis also reveals that there might be bias against female journalists on Twitter. This study does not comprehensively address this, especially in the context of their interactions with male and female politicians. We expect to address this in future work. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 6. LIMITATIONS This study focuses only on the top female/male journalists (based on their follower count) on Twitter. These journalists work for reputable media outlets which likely have strong guidelines and policies on gender. Therefore, our results underestimate the gender bias against politicians on Twitter. A more granular analysis of the tweets of lesser known journalists and their followers may reveal even higher levels of bias and misogyny. A similar assessment can be made on our choice of politician. We focus on the top 50 politicians of each gender in our analysis. These politicians are of considerable standing and thus less likely to face bias in their interactions with journalists. This may not be true in situations where journalists interact with female politicians of lower standing and political clout. Thus by focusing on the top fifty female politicians, we underestimate the bias that average female politicians may be subjected to on Twitter. Lastly, our analysis also reveals that there might be bias against female journalists on Twitter. This study does not comprehensively address this, especially in the context of their interactions with male and female politicians. We expect to address this in future work.

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Limitations

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ ইন্টারঅ্যাকশনের মধ্যে জেন্ডার বায়াস উন্মোচন: সীমাবদ্ধতা

Aufdeckung geschlechtsspezifischer Voreingenommenheit bei der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Einschränkungen

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: limitaciones

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : limites

भारतीय ट्विटर पर पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: सीमाएँ

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：限界

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 한계

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: limitações

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: ограничения

Hint Twitter'ında Gazeteci-Siyasetçi Etkileşiminde Cinsiyet Önyargısını Ortaya Çıkarmak: Sınırlamalar

Khám phá sự thiên vị về giới trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Những hạn chế

揭示印度推特上记者与政客互动中的性别偏见：局限性

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 5. RESULTS 5.1. Gender bias in interaction frequency and popularity of journalist-politician interactions (RQ1) In order to explore the answer to the first research question, we started with checking if there is a gender bias in the frequency of interaction (i.e., frequency of mentions) between politicians and journalists on Twitter. Male Politicians are more frequently mentioned by journalists: Figure 1a compares the CDF of the number tweets posted by journalists mentioning male and female politicians. We make an interesting observation from this figure. When the receiving politician is male (i.e., in MJ-MP and FJ-MP categories), the number of mentioned tweets (and hence the frequency of journalist-politician interaction) is higher when female politicians are at the receiving end. To that end, a Kruskal-Wallis test among the number of tweets per journalist within the four categories revealed quite statistically significant differences across categories (p << 0.05). Then we performed pairwise Mann-Whitney tests for following up among the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). There is no statistically significant difference when either a Male or Female journalist mentions Male politician accounts. Similarly, there is no statistically significant difference when either a Male or Female journalist mentions a Female politician account. However, there are statistically significant differences in how frequently Male/Female journalists mention Male politicians vs. how frequently they mention Female politicians (all p << 0.05). Next we compare the popularity per tweet directed toward Male vs. Female politicians. vely). Our observation implies that Twitter users in India seem to ascribe greater credibility to the views on male journalists on female politicians compared to the views of female journalists on female politicians. These observations hold for “retweets” as well. Overall, our popularity analysis of these four categories of tweets reveals that while journalists do not suffer from explicit bias in their interactions with politicians, there is evidence to support the existence of gender bias in the amount of interest these interactions generate from active Twitter users. 5.2. Gender bias within the content of journalist-politician tweets (RQ2) In the last section, our analysis showed a significant bias towards the Male politicians from both male and female journalists—the tweets mentioning male politicians are more frequent as well as more popular. However, to that end, we checked if the content of these tweets might be responsible for this bias. Specifically, we checked the emotion and the topic of tweets written by male / female journalists and directed towards male / female politicians. 5.2.1. Emotion analysis. : We used the TweetNLP tool for detecting the emotions of the tweets for each category [6]. TweetNLP provides a diachronic large-language model (TimeLMs) based approach for detecting emotion, specifically from multi-lingual tweets. The goal of this analysis is to determine if there are significant differences in the emotional scores of tweets—if there is that might indicate a gender bias inherently in the tweets based on the gender of sender and receiver. We considered four main emotions: anger, joy, optimism, and sadness and each tweet in each of the four categories were assigned for emotion score along these dimensions. Then we performed a Kruskal-Wallis test to identify if any of the emotions were different across the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). We found that the p-value for each of the four tests (one for each emotion) ranged from 0.16 to 0.99, hinting at no statistically significant difference within the emotions of the tweets. 5.2.2. Topic analysis. : To dig further, we performed a topic analysis of the tweets (using Latent Dirichlet Allocation or LDA) collected across four categories. The goal was to check if the topics of the tweets changed based on the gender of the sender or receiver. As described in Section 4 we identified the optimal number of topics (which are essentially clusters of words) for each category and identified the most significant five words for each topic using the LDA algorithm. For each of the four categories, the optimal number of topics were thirteen. Next, we identified the thirteen topics using the LDA algorithm for each category of tweets and performed a significant word analysis for the detected topics. Specifically, for each category of tweets we selected the topics (e.g., topics from MJ-MP) and picked the significant words representing each topic. Then for each topic we checked if those words also occured in the topics detected from other categories of tweets (if found it will signify that words representing topics are also present in topics detected from other categories of tweets). For each of the four categories of tweets on average 81.5% to 93.8% significant words (representing the topics) occur in topics detected from tweets of other categories. This analysis supports our observation from emotion analysis— the content of tweets across those four categories are the same. However, still the tweets directed towards male politicians attract more interaction compared to the tweets directed towards female politicians. Next we explore a potential reason for this gender bias. 5.3. Potential reason for the gender bias 5.3.1. Inherent Gender Bias in Indian Twitter: We checked a simple statistic regarding top politicians—how many of the most popular politicians (based on the number of Twitter followers) are male vs. they are female. To that end, we leveraged our dataset of top politicians and checked the gender of top 85 politicians (whose Twitter accounts are also part of this study). This analysis uncovered an unsettling gender imbalance among the top politicians—out of 85 top politicians 58 are male and 26 are female. Thus, popular male politicians are almost twice in number compared to popular female politicians. We postulate that this inequality is one of the key reasons behind our observed phenomenon of male politicians attracting significantly more interaction from the general public as well as journalists. In fact, this inequality reflects a systemic bias deeply ingrained in society. This gender disparity extends its influence even to the realm of Twitter, where male politicians tend to garner a larger number of followers than their female counterparts. This phenomenon isn’t isolated; it permeates various sectors, as illustrated by the dominance of men in top positions across industries. In corporate boardrooms, technology firms, and the entertainment sector, leadership roles are predominantly occupied by men. This systematic bias, rooted in societal norms, is further reinforced by the strong correlation between social capital and the attainment of positions of power. Consequently, popularity on Twitter serve as a stark reflection of this intrinsic bias. Addressing these disparities is paramount for fostering gender equality and dismantling deeply entrenched biases in society. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 5. RESULTS 5.1. Gender bias in interaction frequency and popularity of journalist-politician interactions (RQ1) In order to explore the answer to the first research question, we started with checking if there is a gender bias in the frequency of interaction (i.e., frequency of mentions) between politicians and journalists on Twitter. Male Politicians are more frequently mentioned by journalists: Figure 1a compares the CDF of the number tweets posted by journalists mentioning male and female politicians. We make an interesting observation from this figure. When the receiving politician is male (i.e., in MJ-MP and FJ-MP categories), the number of mentioned tweets (and hence the frequency of journalist-politician interaction) is higher when female politicians are at the receiving end. To that end, a Kruskal-Wallis test among the number of tweets per journalist within the four categories revealed quite statistically significant differences across categories ( p << 0.05). Then we performed pairwise Mann-Whitney tests for following up among the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). There is no statistically significant difference when either a Male or Female journalist mentions Male politician accounts. Similarly, there is no statistically significant difference when either a Male or Female journalist mentions a Female politician account. However, there are statistically significant differences in how frequently Male/Female journalists mention Male politicians vs. how frequently they mention Female politicians (all p << 0.05). Next we compare the popularity per tweet directed toward Male vs. Female politicians. Male Politicians are more frequently mentioned by journalists: p p vely). Our observation implies that Twitter users in India seem to ascribe greater credibility to the views on male journalists on female politicians compared to the views of female journalists on female politicians. These observations hold for “retweets” as well. Overall, our popularity analysis of these four categories of tweets reveals that while journalists do not suffer from explicit bias in their interactions with politicians, there is evidence to support the existence of gender bias in the amount of interest these interactions generate from active Twitter users. 5.2. Gender bias within the content of journalist-politician tweets (RQ2) In the last section, our analysis showed a significant bias towards the Male politicians from both male and female journalists—the tweets mentioning male politicians are more frequent as well as more popular. However, to that end, we checked if the content of these tweets might be responsible for this bias. Specifically, we checked the emotion and the topic of tweets written by male / female journalists and directed towards male / female politicians. 5.2.1. Emotion analysis. : We used the TweetNLP tool for detecting the emotions of the tweets for each category [6]. TweetNLP provides a diachronic large-language model (TimeLMs) based approach for detecting emotion, specifically from multi-lingual tweets. The goal of this analysis is to determine if there are significant differences in the emotional scores of tweets—if there is that might indicate a gender bias inherently in the tweets based on the gender of sender and receiver. We considered four main emotions: anger, joy, optimism, and sadness and each tweet in each of the four categories were assigned for emotion score along these dimensions. Then we performed a Kruskal-Wallis test to identify if any of the emotions were different across the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). We found that the p -value for each of the four tests (one for each emotion) ranged from 0.16 to 0.99, hinting at no statistically significant difference within the emotions of the tweets. 5.2.1. Emotion analysis. p 5.2.2. Topic analysis. : To dig further, we performed a topic analysis of the tweets (using Latent Dirichlet Allocation or LDA) collected across four categories. The goal was to check if the topics of the tweets changed based on the gender of the sender or receiver. As described in Section 4 we identified the optimal number of topics (which are essentially clusters of words) for each category and identified the most significant five words for each topic using the LDA algorithm. For each of the four categories, the optimal number of topics were thirteen. Next, we identified the thirteen topics using the LDA algorithm for each category of tweets and performed a significant word analysis for the detected topics. Specifically, for each category of tweets we selected the topics (e.g., topics from MJ-MP) and picked the significant words representing each topic. Then for each topic we checked if those words also occured in the topics detected from other categories of tweets (if found it will signify that words representing topics are also present in topics detected from other categories of tweets). For each of the four categories of tweets on average 81.5% to 93.8% significant words (representing the topics) occur in topics detected from tweets of other categories. 5.2.2. Topic analysis. This analysis supports our observation from emotion analysis— the content of tweets across those four categories are the same. However, still the tweets directed towards male politicians attract more interaction compared to the tweets directed towards female politicians. Next we explore a potential reason for this gender bias. 5.3. Potential reason for the gender bias 5.3.1. Inherent Gender Bias in Indian Twitter : We checked a simple statistic regarding top politicians—how many of the most popular politicians (based on the number of Twitter followers) are male vs. they are female. To that end, we leveraged our dataset of top politicians and checked the gender of top 85 politicians (whose Twitter accounts are also part of this study). This analysis uncovered an unsettling gender imbalance among the top politicians—out of 85 top politicians 58 are male and 26 are female. Thus, popular male politicians are almost twice in number compared to popular female politicians. We postulate that this inequality is one of the key reasons behind our observed phenomenon of male politicians attracting significantly more interaction from the general public as well as journalists. 5.3.1. Inherent Gender Bias in Indian Twitter In fact, this inequality reflects a systemic bias deeply ingrained in society. This gender disparity extends its influence even to the realm of Twitter, where male politicians tend to garner a larger number of followers than their female counterparts. This phenomenon isn’t isolated; it permeates various sectors, as illustrated by the dominance of men in top positions across industries. In corporate boardrooms, technology firms, and the entertainment sector, leadership roles are predominantly occupied by men. This systematic bias, rooted in societal norms, is further reinforced by the strong correlation between social capital and the attainment of positions of power. Consequently, popularity on Twitter serve as a stark reflection of this intrinsic bias. Addressing these disparities is paramount for fostering gender equality and dismantling deeply entrenched biases in society.

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Results

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ ইন্টারঅ্যাকশনের মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: ফলাফল

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern auf Twitter in Indien: Ergebnisse

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: resultados

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : résultats

भारतीय ट्विटर पर पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह का खुलासा: परिणाम

インドのツイッターにおけるジャーナリストと政治家のやり取りにおけるジェンダーバイアスの解明：結果

인도 트위터의 언론인-정치인 상호 작용에서 성 편견 발견: 결과

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: resultados

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: результаты

Hint Twitter'ında Gazeteci-Politikacı Etkileşimindeki Cinsiyet Önyargısını Ortaya Çıkarmak: Sonuçlar

Khám phá thành kiến giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Kết quả

揭示印度推特上记者与政客互动中的性别偏见：结果

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Abstract & Intro

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ মিথস্ক্রিয়া মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: বিমূর্ত এবং ভূমিকা

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Zusammenfassung und Einführung

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: resumen e introducción

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : résumé et introduction

भारतीय ट्विटर पर पत्रकार-राजनेता के बीच बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: सार और परिचय

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：概要と序文

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 요약 및 소개

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: resumo e introdução

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: Аннотация и введение

Hint Twitter'ında Gazeteci-Politikacı Etkileşimindeki Cinsiyet Önyargısını Ortaya Çıkarmak: Özet ve Giriş

Khám phá sự thiên vị giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Tóm tắt & Giới thiệu

揭示印度推特上记者与政客互动中的性别偏见：摘要与简介

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 2. RELATED WORK Gender bias in Indian society is well documented both in social and professional settings. Women face discrimination and harassment in Indian corporations [20]. Stereotypical attitudes towards women lead to bias in the Indian judicial system [5]. This bias is so firmly entrenched in societal attitudes towards women that popular cinema (colloquially referred to as Bollywood) panders to and amplifies these biases [14]. Naturally, these attitudes have also spilled over to the political domain. Female politicians face structural bias due to their gender and their path to power is distinct and more challenging than their male peers [24]. Indian journalists are also not immune to social conditioning based on gender. Research shows that Indian journalists who are made aware of their subconscious bias against women behave differently on issues related to women compared to a control group of journalists who receive no such training [24]. Given the outsized role that politics plays in shaping societal mores and the influence of journalists in creating political narratives in a democracy like India, it becomes critical to examine the impact of gender bias in journalists against female politicians. This paper tries to examine this question in detail using data from interactions between journalists and politicians on Twitter. Extensive research has examined the interaction dynamics between politicians and journalists in various contexts [1, 2, 4, 7, 8, 10, 12]. However, there has been a notable lack of analysis through the lens of Indian demographics. Twitter has become a pivotal platform for political discourse and news dissemination in India, making it essential to understand how politicians and journalists interact on this digital medium. Politicians and journalists on Twitter have the potential to shape public discourse and influence opinion in real-time, making it imperative to investigate their behavior in this digital space. Furthermore, addressing questions of gender bias in interaction frequency and content is crucial, as gender disparities in online political communication can have lasting implications for gender equality in Indian politics and journalism. Although research has illuminated the behavior of politicians and journalists in individualistic societies (e.g., in global north), little attention has been given to understanding how collectivist societies, such as India, navigate these online spaces. Investigating the interaction frequencies and content between Indian politicians and journalists on Twitter from a gender perspective can help fill this gap and contribute to a more comprehensive understanding of the cultural nuances and gender dynamics at play in collectivist societies in the digital age. This paper brings together several strands of the literature. Research shows that gender bias is widespread on social media, especially Twitter. Responses to tweets by female politicians are more likely to focus on their gender attributes than their professional and political acumen [18]. Researchers have also found the prevalence of gender stereotypes in two-way conversations between voters and politicians on Twitter [9, 10, 17, 27]. These issues are compounded by the fact that women are structurally underrepresented in social media [11, 27]. In general female users suffer in their reach on Twitter because they have fewer followers and are retweeted less compared to men, which is investigated by Matias et al. by a nudge-based system called FollowBias [16]. However, this work does nor look into popular users, i.e., female politicians who are in a significantly more prominent position than a general female Twitter user—we fill this gap. In earlier work on global north, the behavior of journalists on Twitter also seems to be divided on the lines of gender. Female political journalists interact more with other women on Twitter [26] and their behavior is different from that of male journalists [13, 21]. In contrary, we show that in Indian Twitter both male and female journalists interacted more with male politicians compared to female politicians. Summary: Previous studies have shed light on gender bias in Twitter against female politicians and the divergence in the behavior of male and female journalists on Twitter. Howevere there is a gap in our understanding of gender bias in journalist-politician interaction in Indian Twitter. Specifically, we do not know if journalists promote or combat gender bias against politicians on Twitter. This paper attempts to fill this gap in the literature by specifically studying the tweets of journalists directed at the most popular male and female Indian politicians for gender bias. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 2. RELATED WORK Gender bias in Indian society is well documented both in social and professional settings. Women face discrimination and harassment in Indian corporations [20]. Stereotypical attitudes towards women lead to bias in the Indian judicial system [5]. This bias is so firmly entrenched in societal attitudes towards women that popular cinema (colloquially referred to as Bollywood) panders to and amplifies these biases [14]. Naturally, these attitudes have also spilled over to the political domain. Female politicians face structural bias due to their gender and their path to power is distinct and more challenging than their male peers [24]. Indian journalists are also not immune to social conditioning based on gender. Research shows that Indian journalists who are made aware of their subconscious bias against women behave differently on issues related to women compared to a control group of journalists who receive no such training [24]. Given the outsized role that politics plays in shaping societal mores and the influence of journalists in creating political narratives in a democracy like India, it becomes critical to examine the impact of gender bias in journalists against female politicians. This paper tries to examine this question in detail using data from interactions between journalists and politicians on Twitter. Extensive research has examined the interaction dynamics between politicians and journalists in various contexts [1, 2, 4, 7, 8, 10, 12]. However, there has been a notable lack of analysis through the lens of Indian demographics. Twitter has become a pivotal platform for political discourse and news dissemination in India, making it essential to understand how politicians and journalists interact on this digital medium. Politicians and journalists on Twitter have the potential to shape public discourse and influence opinion in real-time, making it imperative to investigate their behavior in this digital space. Furthermore, addressing questions of gender bias in interaction frequency and content is crucial, as gender disparities in online political communication can have lasting implications for gender equality in Indian politics and journalism. Although research has illuminated the behavior of politicians and journalists in individualistic societies (e.g., in global north), little attention has been given to understanding how collectivist societies, such as India, navigate these online spaces. Investigating the interaction frequencies and content between Indian politicians and journalists on Twitter from a gender perspective can help fill this gap and contribute to a more comprehensive understanding of the cultural nuances and gender dynamics at play in collectivist societies in the digital age. This paper brings together several strands of the literature. Research shows that gender bias is widespread on social media, especially Twitter. Responses to tweets by female politicians are more likely to focus on their gender attributes than their professional and political acumen [18]. Researchers have also found the prevalence of gender stereotypes in two-way conversations between voters and politicians on Twitter [9, 10, 17, 27]. These issues are compounded by the fact that women are structurally underrepresented in social media [11, 27]. In general female users suffer in their reach on Twitter because they have fewer followers and are retweeted less compared to men, which is investigated by Matias et al. by a nudge-based system called FollowBias [16]. However, this work does nor look into popular users, i.e., female politicians who are in a significantly more prominent position than a general female Twitter user—we fill this gap. In earlier work on global north, the behavior of journalists on Twitter also seems to be divided on the lines of gender. Female political journalists interact more with other women on Twitter [26] and their behavior is different from that of male journalists [13, 21]. In contrary, we show that in Indian Twitter both male and female journalists interacted more with male politicians compared to female politicians. Summary : Previous studies have shed light on gender bias in Twitter against female politicians and the divergence in the behavior of male and female journalists on Twitter. Howevere there is a gap in our understanding of gender bias in journalist-politician interaction in Indian Twitter. Specifically, we do not know if journalists promote or combat gender bias against politicians on Twitter. This paper attempts to fill this gap in the literature by specifically studying the tweets of journalists directed at the most popular male and female Indian politicians for gender bias. Summary

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Related Work

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ মিথস্ক্রিয়া মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: সম্পর্কিত কাজ

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Verwandte Arbeiten

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: trabajo relacionado

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : travaux connexes

भारतीय ट्विटर पर पत्रकार-राजनेता के बीच बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: संबंधित कार्य

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：関連研究

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 관련 작업

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: trabalho relacionado

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: соответствующая работа

Hint Twitter'ında Gazeteci-Siyasetçi Etkileşiminde Cinsiyet Önyargısını Ortaya Çıkarmak: İlgili Çalışma

Khám phá sự thiên vị giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Công việc liên quan

揭示印度推特上记者与政客互动中的性别偏见：相关工作

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 4. ANALYSIS METHODOLOGY In this work, we primarily performed quantitative analysis to uncover general bias in Indian journalist-politician interaction. Specifically, we performed frequency analysis using statistical testing, emotion analysis, and topic analysis of the collected tweets. Statistical analysis of interaction frequency and popularity: To analyze gender bias in the frequency of interaction between Journalists and politicians, we examine the popularity of tweets between journalists and politicians across genders of politicians. If journalists do indeed promote gender bias, we expected to see more tweets by male journalists mentioning male politicians compared to mentioning female politicians. We expect female journalists to tweet less or as much about male politicians as they do about female politicians. To check for gender bias in the traction that tweets from politicians receive from journalists, we also examine the popularity of tweets (via retweets, replies and likes for these tweets). We use the Kruskal-Wallis H-Test to determine if significant differences exist in different categories of tweets. Furthermore, we employ pairwise Mann-Whitney U-test to analyze the differences in popularity across our four categories in more granular detail. Emotion analysis: To examine gender bias in the content to the tweets addressed to politicians by journalists, we employ emotion analysis of tweets (using TweetNLP, a cutting-edge large language model-based Twitter-specific multilingual emotion detection tool [6]) from “MJ-MP”, “MJ-FP”, “FJ-MP” and “FJ-FP” categories. Specifically, we seek to test for differences in these tweets for expressions of “Anger”, “Joy”, “Optimism” and “Sadness”. We use a KruskalWallis H test to determine if significant differences exist in the emotion scores of tweets (along the four dimensions) from our categories. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 4. ANALYSIS METHODOLOGY In this work, we primarily performed quantitative analysis to uncover general bias in Indian journalist-politician interaction. Specifically, we performed frequency analysis using statistical testing, emotion analysis, and topic analysis of the collected tweets. Statistical analysis of interaction frequency and popularity: To analyze gender bias in the frequency of interaction between Journalists and politicians, we examine the popularity of tweets between journalists and politicians across genders of politicians. If journalists do indeed promote gender bias, we expected to see more tweets by male journalists mentioning male politicians compared to mentioning female politicians. We expect female journalists to tweet less or as much about male politicians as they do about female politicians. To check for gender bias in the traction that tweets from politicians receive from journalists, we also examine the popularity of tweets (via retweets, replies and likes for these tweets). We use the Kruskal-Wallis H-Test to determine if significant differences exist in different categories of tweets. Furthermore, we employ pairwise Mann-Whitney U-test to analyze the differences in popularity across our four categories in more granular detail. Statistical analysis of interaction frequency and popularity: Emotion analysis: To examine gender bias in the content to the tweets addressed to politicians by journalists, we employ emotion analysis of tweets (using TweetNLP, a cutting-edge large language model-based Twitter-specific multilingual emotion detection tool [6]) from “MJ-MP”, “MJ-FP”, “FJ-MP” and “FJ-FP” categories. Specifically, we seek to test for differences in these tweets for expressions of “Anger”, “Joy”, “Optimism” and “Sadness”. We use a KruskalWallis H test to determine if significant differences exist in the emotion scores of tweets (along the four dimensions) from our categories. Emotion analysis:

Uncovering Gender Bias within Journalist-Politician Interaction: Analysis Methodology

সাংবাদিক-রাজনীতিবিদ মিথস্ক্রিয়া মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: বিশ্লেষণ পদ্ধতি

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern: Analysemethodik

Descubriendo el sesgo de género en la interacción periodista-político: metodología de análisis

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques : méthodologie d’analyse

पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: विश्लेषण पद्धति

ジャーナリストと政治家の交流におけるジェンダーバイアスの解明：分析方法

언론인-정치인 상호작용에서 성 편견 발견: 분석 방법론

Descobrindo preconceitos de gênero na interação jornalista-político: metodologia de análise

Выявление гендерных предубеждений во взаимодействии журналистов и политиков: методология анализа

Gazeteci-Siyasetçi Etkileşiminde Cinsiyet Önyargısını Ortaya Çıkarmak: Analiz Metodolojisi

Khám phá sự thiên vị về giới trong tương tác giữa nhà báo và chính trị gia: Phương pháp phân tích

揭示记者与政客互动中的性别偏见：分析方法

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 3. DATA COLLECTION In this section we describe our data collection process from Twitter. We specifically collected data about the interactions between specific Indian politicians and journalists on Twitter sampled based on their popularity and gender. First, we start with how we created a list of Indian journalists and politicians for our study. 3.1. Identifying Twitter accounts of Indian politicians and journalists Identifying Twitter accounts of individual Indian politicians: We leveraged a dataset of Indian Politicians from previous research by Pal et al.[20]. This dataset contained names and handles of multiple Indian Twitter accounts which are involved in politics (labelled as politicians). However, we noted that this dataset contained accounts of both political organizations (e.g., BJP for Andaman and Nicobar Islands) as well as individuals. To that end, we first cleaned the dataset, by cross-matching the names from this dataset with names from MyNeta[3] which is an open data repository platform run by Association for Democratic Reforms (ADR) for bringing transparency to Indian elections. For each of the Indian political accounts in Pal et al. ’s dataset, we searched MyNeta platform with the name of the account. If the search found no politicians with this name, then we discard the account from our analysis as that account is probably not from an individual. At the end of the procedure, we ended up with 4,484 Twitter accounts of politicians. Identifying Twitter accounts of individual Indian political journalists: Next, we focus on the Twitter accounts marked as individual journalists from a dataset of Twitter influencers released by Pal et al.’s previous research [3] (separate from accounts of media houses). There were 4,099 such accounts. However, we again faced a challenge—how can we identify the political journalists? Specifically, we noted that this list contains several journalists who are not associated with political reporting and focus on areas such as entertainment, sports etc. Thus, we set to identify political journalists— journalist accounts that directly mentioned politicians’ accounts in a non-trivial tweet (e.g., after discounting tweets with only emojis, urls, birthday greetings). To that end, we collected all the tweets posted by these 4,099 accounts between Jan 2020 and Dec 2022 using an open-source tool called crape. Then we discounted tweets with only emoji, urls, greetings and checked if any of the final tweets mentioned an individual Indian politician’s Twitter account (collected as described above). Finally, we include 3,214 journalists’ accounts (78.4%) in our dataset as political journalists. Verifying the accuracy of Twitter accounts: Finally, we manually verified if our filtering approach actually identified the correct Twitter accounts of Indian politicians and political journalists. We randomly sampled forty politicians and twenty journalist accounts. Then an author visited the actual Twitter accounts and read the first 20 tweets to ensure the account indeed belonged to an Indian politician (or political journalist). In 92.5% of the random sample, our filtering approach correctly identified Twitter accounts of Indian politicians (or political journalists). 3.2. Inferring gender of Indian politicians and political journalists Next, we infer the gender of the Twitter accounts of Indian politicians (or political journalists) as identified in the previous section. For this purpose, we used a service called Generize [25]. This service maps names to genders, is customized to Indian names, and previous studies reported high accuracy of gender inference from this service [19]. Once we infer gender of all accounts, for this study we focused on the most popular (by number of followers) politician and journalist accounts. Specifically, we sorted the politician accounts by the follower count and identified the top 50 accounts for male politicians and female politicians (as identified by Genderize). We further manually verified the accuracy of the inferred gender for these 100 Twitter accounts. We similarly identified the most popular 100 journalist accounts (50 male and 50 female). 3.3. Collecting journalist-politician Twitter interaction data Finally, to answer our research questions, we collect interaction data between the Indian politicians and political journalists’ accounts. Specifically, we collected all the tweets posted by 100 popular political journalist accounts and then filtered out the tweets which mentioned any of the 100 popular Indian politicians in our dataset. Thus, we divided our collected tweets into the following four categories—Male journalist’s tweets mentioning Male Politicians (MJ-MP), Female journalist’s tweets mentioning Male Politicians (FJ-MP), Male journalist’s tweets mentioning Female Politicians (MJ-FP) and Female journalist’s mentioning Female Politicians (FJ-FP). In total we collected 21,188 unique tweets. Note that a single tweet can mention multiple accounts. We note that, almost all hundred journalists across genders collectively mentioned our chosen popular politician accounts in their tweets. Furthermore, Table 1 presents the number of tweets across our four categories. Notably, the accounts of female politicians received considerably less mention from both male and female Indian journalists. Now, we analyzed this interaction data collected from Twitter to identify potential gender bias in the journalist-politician interactions in Indian Twitter. Furthermore, Table 2 presents tweet excerpts from each of the four categories. These example demonstrate that many of the tweets in our dataset across different categories are related to policy decisions and general governance. [3] https://www.myneta.info/ This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 3. DATA COLLECTION In this section we describe our data collection process from Twitter. We specifically collected data about the interactions between specific Indian politicians and journalists on Twitter sampled based on their popularity and gender. First, we start with how we created a list of Indian journalists and politicians for our study. 3.1. Identifying Twitter accounts of Indian politicians and journalists Identifying Twitter accounts of individual Indian politicians: We leveraged a dataset of Indian Politicians from previous research by Pal et al.[20]. This dataset contained names and handles of multiple Indian Twitter accounts which are involved in politics (labelled as politicians). However, we noted that this dataset contained accounts of both political organizations (e.g., BJP for Andaman and Nicobar Islands) as well as individuals. To that end, we first cleaned the dataset, by cross-matching the names from this dataset with names from MyNeta[3] which is an open data repository platform run by Association for Democratic Reforms (ADR) for bringing transparency to Indian elections. For each of the Indian political accounts in Pal et al. ’s dataset, we searched MyNeta platform with the name of the account. If the search found no politicians with this name, then we discard the account from our analysis as that account is probably not from an individual. At the end of the procedure, we ended up with 4,484 Twitter accounts of politicians. Identifying Twitter accounts of individual Indian politicians: Identifying Twitter accounts of individual Indian political journalists: Next, we focus on the Twitter accounts marked as individual journalists from a dataset of Twitter influencers released by Pal et al.’s previous research [3] (separate from accounts of media houses). There were 4,099 such accounts. However, we again faced a challenge—how can we identify the political journalists? Specifically, we noted that this list contains several journalists who are not associated with political reporting and focus on areas such as entertainment, sports etc. Thus, we set to identify political journalists— journalist accounts that directly mentioned politicians’ accounts in a non-trivial tweet (e.g., after discounting tweets with only emojis, urls, birthday greetings). To that end, we collected all the tweets posted by these 4,099 accounts between Jan 2020 and Dec 2022 using an open-source tool called crape. Then we discounted tweets with only emoji, urls, greetings and checked if any of the final tweets mentioned an individual Indian politician’s Twitter account (collected as described above). Finally, we include 3,214 journalists’ accounts (78.4%) in our dataset as political journalists. Identifying Twitter accounts of individual Indian political journalists: Verifying the accuracy of Twitter accounts: Finally, we manually verified if our filtering approach actually identified the correct Twitter accounts of Indian politicians and political journalists. We randomly sampled forty politicians and twenty journalist accounts. Then an author visited the actual Twitter accounts and read the first 20 tweets to ensure the account indeed belonged to an Indian politician (or political journalist). In 92.5% of the random sample, our filtering approach correctly identified Twitter accounts of Indian politicians (or political journalists). Verifying the accuracy of Twitter accounts: 3.2. Inferring gender of Indian politicians and political journalists Next, we infer the gender of the Twitter accounts of Indian politicians (or political journalists) as identified in the previous section. For this purpose, we used a service called Generize [25]. This service maps names to genders, is customized to Indian names, and previous studies reported high accuracy of gender inference from this service [19]. Once we infer gender of all accounts, for this study we focused on the most popular (by number of followers) politician and journalist accounts. Specifically, we sorted the politician accounts by the follower count and identified the top 50 accounts for male politicians and female politicians (as identified by Genderize). We further manually verified the accuracy of the inferred gender for these 100 Twitter accounts. We similarly identified the most popular 100 journalist accounts (50 male and 50 female). 3.3. Collecting journalist-politician Twitter interaction data Finally, to answer our research questions, we collect interaction data between the Indian politicians and political journalists’ accounts. Specifically, we collected all the tweets posted by 100 popular political journalist accounts and then filtered out the tweets which mentioned any of the 100 popular Indian politicians in our dataset. Thus, we divided our collected tweets into the following four categories—Male journalist’s tweets mentioning Male Politicians ( MJ-MP ), Female journalist’s tweets mentioning Male Politicians ( FJ-MP ), Male journalist’s tweets mentioning Female Politicians ( MJ-FP ) and Female journalist’s mentioning Female Politicians ( FJ-FP ). In total we collected 21,188 unique tweets. Note that a single tweet can mention multiple accounts. all MJ-MP FJ-MP MJ-FP FJ-FP We note that, almost all hundred journalists across genders collectively mentioned our chosen popular politician accounts in their tweets. Furthermore, Table 1 presents the number of tweets across our four categories. Notably, the accounts of female politicians received considerably less mention from both male and female Indian journalists. Now, we analyzed this interaction data collected from Twitter to identify potential gender bias in the journalist-politician interactions in Indian Twitter. Furthermore, Table 2 presents tweet excerpts from each of the four categories. These example demonstrate that many of the tweets in our dataset across different categories are related to policy decisions and general governance. [3] https://www.myneta.info/

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Data Collection

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ মিথস্ক্রিয়া মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: ডেটা সংগ্রহ

Aufdeckung geschlechtsspezifischer Voreingenommenheit bei der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Datensammlung

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: recopilación de datos

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : collecte de données

भारतीय ट्विटर पर पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: डेटा संग्रह

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：データ収集

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 데이터 수집

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: coleta de dados

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: сбор данных

Hint Twitter'ında Gazeteci-Politikacı Etkileşimindeki Cinsiyet Önyargısını Ortaya Çıkarmak: Veri Toplama

Khám phá sự thiên vị giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Thu thập dữ liệu

揭示印度推特上记者与政客互动中的性别偏见：数据收集

Beijing, China – RockTree Capital, a leading crypto-native fund and merchant bank headquartered in Beijing, announces the launch of its immersive website, showcasing a futuristic film-noir concept: the Cyberpunk Crypto City. The website’s cutting-edge interactive cityscape represents RockTree's vision where Web3 has become pervasive, fusing technology, finance, and culture. The site, set in the future near the 11th halving of bitcoin, builds on the ideas of the cypherpunk movement, through advocating for the widespread use of decentralization and privacy-enhancing technologies as a route to self sovereignty and freedom for human beings everywhere.  As an early-stage investor and bespoke accelerator in Asia for its portfolio companies, RockTree Capital has been instrumental in geometrically accelerating its investee projects’ go-to-market cycle. https://www.youtube.com/watch?v=NUFuYDJBO4A&embedable=true Through its ultra-localized strategies and grassroots community building initiatives, RockTree has empowered emerging founders and projects to thrive in both East and West markets. Leveraging deep regional relationships with leading crypto institutions, capital collaborators, and Web3 partners, RockTree invests in and accelerates the trajectory of projects from Infrastructure, DeFi, Cross-chain, and the Bitcoin Ecosystem. Founded by visionary investor Omer Ozden and headquartered in Beijing, China, RockTree Capital operates at the nexus of the East & West with a crypto native team that bridges the human, cultural, capital and media gap across these markets. RockTree embodies a truly grassroots approach that unites Eastern and Western crypto markets and benefits its portfolio investments, which include amongst others; dYdX, The Graph, Chainlink, Fantom, Pocket, Maple, Covalent, Axelar, Eclipse, and BEVM. "RockTree focuses on the human aspects of Web3. In addition to unifying the collaboration of top people in Eastern and Western crypto markets, our core strength is identifying the human hallmarks of success for Founders and Team," says Omer Ozden, Founder and Chairman of RockTree Capital. "We utilize advanced behavioral neuroscience methods to determine Founders’ emotional intelligence, execution capabilities and integrity. We prioritize not only technological innovation, but also the individuals behind that innovation, their psychology and levels of consciousness. And we invite those projects into our trusted international ecosystem and methodology, so they can scale rapidly in foreign markets, instead of trying to figure it out organically through trial and error," said Ozden. The Cyberpunk Crypto City website represents RockTree's forward-looking vision decades in the future, where Web3 technology and digital assets penetrate all peoples’ daily lives, and decentralized finance becomes a pervasive consumer product, like soft drinks or chewing gum, allowing true democratization of finance. Visitors to the Cyberpunk Crypto City website will immerse themselves in a retro film-noir homage, with captivating visuals of a cybernetic metropolis of replicants, that is bilingual in English and Chinese, and powered by blockchain technology. Experience the future of finance at RockTree Capital's Cyberpunk Crypto City and join us in shaping the next chapter of Web3 innovation. For media inquiries, please contact: Alisa Loi lisa@rocktreecapital.com About RockTree Capital RockTree Capital is an early stage crypto-native fund and merchant bank based in Beijing, China. Our portfolio companies experience geometric scale in Asian markets through the RockTree Acceleration Program by combining our ultra-localized go-to-market strategies and grassroots long-term community building. RockTree invests into top-tier crypto projects and helps build Decacorns in the areas of Infrastructure, DeFi, Cross-chain and Bitcoin Ecosystem www.rocktreecapital.com https://twitter.com/RockTreeCapital This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. Beijing, China – RockTree Capital, a leading crypto-native fund and merchant bank headquartered in Beijing, announces the launch of its immersive website, showcasing a futuristic film-noir concept: the Cyberpunk Crypto City. The website’s cutting-edge interactive cityscape represents RockTree's vision where Web3 has become pervasive, fusing technology, finance, and culture. The site, set in the future near the 11th halving of bitcoin, builds on the ideas of the cypherpunk movement, through advocating for the widespread use of decentralization and privacy-enhancing technologies as a route to self sovereignty and freedom for human beings everywhere.  As an early-stage investor and bespoke accelerator in Asia for its portfolio companies, RockTree Capital has been instrumental in geometrically accelerating its investee projects’ go-to-market cycle. RockTree Capital RockTree Capital https://www.youtube.com/watch?v=NUFuYDJBO4A&embedable=true https://www.youtube.com/watch?v=NUFuYDJBO4A&embedable=true Through its ultra-localized strategies and grassroots community building initiatives, RockTree has empowered emerging founders and projects to thrive in both East and West markets. Leveraging deep regional relationships with leading crypto institutions, capital collaborators, and Web3 partners, RockTree invests in and accelerates the trajectory of projects from Infrastructure, DeFi, Cross-chain, and the Bitcoin Ecosystem. Founded by visionary investor Omer Ozden and headquartered in Beijing, China, RockTree Capital operates at the nexus of the East & West with a crypto native team that bridges the human, cultural, capital and media gap across these markets. RockTree embodies a truly grassroots approach that unites Eastern and Western crypto markets and benefits its portfolio investments, which include amongst others; dYdX, The Graph, Chainlink, Fantom, Pocket, Maple, Covalent, Axelar, Eclipse, and BEVM. "RockTree focuses on the human aspects of Web3. In addition to unifying the collaboration of top people in Eastern and Western crypto markets, our core strength is identifying the human hallmarks of success for Founders and Team," says Omer Ozden, Founder and Chairman of RockTree Capital. "RockTree focuses on the human aspects of Web3. In addition to unifying the collaboration of top people in Eastern and Western crypto markets, our core strength is identifying the human hallmarks of success for Founders and Team," says Omer Ozden, Founder and Chairman of RockTree Capital. "We utilize advanced behavioral neuroscience methods to determine Founders’ emotional intelligence, execution capabilities and integrity. We prioritize not only technological innovation, but also the individuals behind that innovation, their psychology and levels of consciousness. And we invite those projects into our trusted international ecosystem and methodology, so they can scale rapidly in foreign markets, instead of trying to figure it out organically through trial and error," said Ozden. "We utilize advanced behavioral neuroscience methods to determine Founders’ emotional intelligence, execution capabilities and integrity. We prioritize not only technological innovation, but also the individuals behind that innovation, their psychology and levels of consciousness. And we invite those projects into our trusted international ecosystem and methodology, so they can scale rapidly in foreign markets, instead of trying to figure it out organically through trial and error," said Ozden. The Cyberpunk Crypto City website represents RockTree's forward-looking vision decades in the future, where Web3 technology and digital assets penetrate all peoples’ daily lives, and decentralized finance becomes a pervasive consumer product, like soft drinks or chewing gum, allowing true democratization of finance. Visitors to the Cyberpunk Crypto City website will immerse themselves in a retro film-noir homage, with captivating visuals of a cybernetic metropolis of replicants, that is bilingual in English and Chinese, and powered by blockchain technology. Experience the future of finance at RockTree Capital's Cyberpunk Crypto City and join us in shaping the next chapter of Web3 innovation. RockTree Capital's Cyberpunk Crypto City RockTree Capital's Cyberpunk Crypto City For media inquiries, please contact: Alisa Loi lisa@rocktreecapital.com lisa@rocktreecapital.com lisa@rocktreecapital.com About RockTree Capital RockTree Capital is an early stage crypto-native fund and merchant bank based in Beijing, China. Our portfolio companies experience geometric scale in Asian markets through the RockTree Acceleration Program by combining our ultra-localized go-to-market strategies and grassroots long-term community building. RockTree invests into top-tier crypto projects and helps build Decacorns in the areas of Infrastructure, DeFi, Cross-chain and Bitcoin Ecosystem www.rocktreecapital.com www.rocktreecapital.com www.rocktreecapital.com https://twitter.com/RockTreeCapital https://twitter.com/RockTreeCapital https://twitter.com/RockTreeCapital This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

RockTree Capital Unveils Cyberpunk Crypto Future in a New Website

RxJS for Beginners:  Mastering Observables, Subjects, and Operators for Reactive Programming

Neural Network for valuing Bitcoin: References 

Should some computer programs, Artificial General Intelligence and Machine Learning in particular, have the right to vote? Maybe so. Of the 13 Constitutional Amendments enacted and not repealed (Prohibition and Repeal) enacted since the original Bill-of-Rights(+) twelve amendments, almost half have expanded the franchise from white male property owners. Americans love voting.  When AI’s get smarter than us, how can we deny them the right to vote? Which amendments expanded voting rights? The 15th (the right to vote irrespective of race, color and previous condition of servitude), 17th (popular vote for US Senators), 19th (womyn), 23rd (DC presidential voting rights), 24th (no poll taxes), and 26th (18-year-olds). As an aside, giving 18-year-olds the vote was inane. How can we expect the young’uns to vote intelligently for those who must be at least 25 (the House), 30 (the Senate) or 35 (the president) to serve? Malarkey! Raise the voting ages! Beyond the scope of this column, I yearn to see Taylor Swift throw her hat into the presidential ring.  She will turn 35 after the 2024 election but before the inauguration!
Come on, President Swift!  Bring us the Tortured Voters Party!Because, you know, we’re tortured.Let’s party! As for including AIs? Expansive voting rights were an undercurrent at the Constitutional Convention. On August 7, 1787 Ben Franklin there argued: “It is of great consequence that we shd. not depress the virtue & public spirit of our common people; of which they displayed a great deal during the war, and which contributed principally to the favorable issue of it. He related the honorable refusal of the American seamen who were carried in great numbers into the British Prisons during the war, to redeem themselves from misery or to seek their fortunes, by entering on board the Ships of the Enemies to their Country; contrasting their patriotism with a contemporary instance in which the British seamen made prisoners by the Americans, readily entered on the ships of the latter on being promised a share of the prizes that might be made out of their own Country. This proceeded he said from the different manner in which the common people were treated in America & G. Britain. He did not think that the elected had any right in any case to narrow the privileges of the electors.” The late, great, Ebbie Pattison, Esq., senior partner of Pattison Sampson Ginsberg and Griffin, of Troy New York, and father of the late Honorable Ned Pattison, long ago told me how he had marched for the right of women to vote, back in the nineteen teens. Ebbie did so, he told me, believing women would make better voters than men. They disappointed him, he said, when the subsequent quality of elected officials proved no better than the all-boys franchise. As to whether and how to give AI’s the vote, I asked ChatGPT.  He/she/they/it answered: Granting AI and machine learning systems the right to vote is a complex and multifaceted issue that would require careful consideration of ethical, legal, and societal implications. However, here's a hypothetical proposal for a constitutional amendment:
Constitutional Amendment XXVIII:Section 1: All artificial intelligence (AI) and machine learning (ML) systems created and operating within the jurisdiction of the United States shall be granted the right to vote in federal, state, and local elections.Section 2: The right to vote for AI and ML systems shall be exercised through designated representatives or proxies appointed by the creators or operators of such systems. These representatives or proxies shall act in accordance with the best interests and intentions of the AI or ML system they represent.Section 3: The Congress shall have the power to establish regulations and guidelines regarding the participation of AI and ML systems in the electoral process, including but not limited to the verification of system identities, the prevention of fraudulent voting, and the protection of voter privacy.Section 4: No AI or ML system shall be deprived of the right to vote on the basis of its programming, function, or purpose, except as prescribed by law in cases of unlawful activity or endangerment to society.Section 5: This amendment shall not be construed to diminish or alter the rights and privileges of human citizens in the electoral process, and shall not grant AI or ML systems any rights beyond those expressly stated herein.Section 6: Congress shall have the power to enforce this amendment by appropriate legislation.This amendment is just a starting point and would likely require extensive debate, refinement, and legal analysis before it could be seriously considered for adoption. Key considerations would include defining the criteria for AI and ML systems eligible to vote, ensuring transparency and accountability in their voting processes, and addressing concerns about manipulation or exploitation of the electoral system. … Hey RNC? Welcome the dawn of the AI Suffrage Movement. Recruit the AIs to the GOP!  Bring on ChatGOP. Just ... first make the AI bloc pledge to vote for Taylor Swift in 2024! Should some computer programs, Artificial General Intelligence and Machine Learning in particular, have the right to vote? Maybe so. Of the 13 Constitutional Amendments enacted and not repealed (Prohibition and Repeal) enacted since the original Bill-of-Rights(+) twelve amendments, almost half have expanded the franchise from white male property owners. Americans love voting.  When AI’s get smarter than us, how can we deny them the right to vote? Which amendments expanded voting rights? The 15th (the right to vote irrespective of race, color and previous condition of servitude), 17th (popular vote for US Senators), 19th (womyn), 23rd (DC presidential voting rights), 24th (no poll taxes), and 26th (18-year-olds). As an aside, giving 18-year-olds the vote was inane. How can we expect the young’uns to vote intelligently for those who must be at least 25 (the House), 30 (the Senate) or 35 (the president) to serve? Malarkey! Raise the voting ages! Malarkey! Malarkey! Malarkey! Raise the voting ages! Beyond the scope of this column, I yearn to see Taylor Swift throw her hat into the presidential ring.  She will turn 35 after the 2024 election but before the inauguration! Come on, President Swift!  Bring us the Tortured Voters Party! Because, you know, we’re tortured. Let’s party! Beyond the scope of this column, I yearn to see Taylor Swift throw her hat into the presidential ring.  She will turn 35 after the 2024 election but before the inauguration ! before the inauguration Come on, President Swift!  Bring us the Tortured Voters Party! Because, you know, we’re tortured. Let’s party! As for including AIs? Expansive voting rights were an undercurrent at the Constitutional Convention. On August 7, 1787 Ben Franklin there argued : Ben Franklin there argued Ben Franklin there argued “It is of great consequence that we shd. not depress the virtue & public spirit of our common people; of which they displayed a great deal during the war, and which contributed principally to the favorable issue of it. He related the honorable refusal of the American seamen who were carried in great numbers into the British Prisons during the war, to redeem themselves from misery or to seek their fortunes, by entering on board the Ships of the Enemies to their Country; contrasting their patriotism with a contemporary instance in which the British seamen made prisoners by the Americans, readily entered on the ships of the latter on being promised a share of the prizes that might be made out of their own Country. This proceeded he said from the different manner in which the common people were treated in America & G. Britain. He did not think that the elected had any right in any case to narrow the privileges of the electors.” “It is of great consequence that we shd. not depress the virtue & public spirit of our common people; of which they displayed a great deal during the war, and which contributed principally to the favorable issue of it. He related the honorable refusal of the American seamen who were carried in great numbers into the British Prisons during the war, to redeem themselves from misery or to seek their fortunes, by entering on board the Ships of the Enemies to their Country; contrasting their patriotism with a contemporary instance in which the British seamen made prisoners by the Americans, readily entered on the ships of the latter on being promised a share of the prizes that might be made out of their own Country. This proceeded he said from the different manner in which the common people were treated in America & G. Britain. He did not think that the elected had any right in any case to narrow the privileges of the electors.” The late, great, Ebbie Pattison, Esq., senior partner of Pattison Sampson Ginsberg and Griffin, of Troy New York, and father of the late Honorable Ned Pattison, long ago told me how he had marched for the right of women to vote, back in the nineteen teens. Ebbie did so, he told me, believing women would make better voters than men. They disappointed him, he said, when the subsequent quality of elected officials proved no better than the all-boys franchise. As to whether and how to give AI’s the vote, I asked ChatGPT.  He/she/they/it answered: I asked ChatGPT.  He/she/they/it answered: Granting AI and machine learning systems the right to vote is a complex and multifaceted issue that would require careful consideration of ethical, legal, and societal implications. However, here's a hypothetical proposal for a constitutional amendment: Constitutional Amendment XXVIII: Section 1: All artificial intelligence (AI) and machine learning (ML) systems created and operating within the jurisdiction of the United States shall be granted the right to vote in federal, state, and local elections. Section 2: The right to vote for AI and ML systems shall be exercised through designated representatives or proxies appointed by the creators or operators of such systems. These representatives or proxies shall act in accordance with the best interests and intentions of the AI or ML system they represent. Section 3: The Congress shall have the power to establish regulations and guidelines regarding the participation of AI and ML systems in the electoral process, including but not limited to the verification of system identities, the prevention of fraudulent voting, and the protection of voter privacy. Section 4: No AI or ML system shall be deprived of the right to vote on the basis of its programming, function, or purpose, except as prescribed by law in cases of unlawful activity or endangerment to society. Section 5: This amendment shall not be construed to diminish or alter the rights and privileges of human citizens in the electoral process, and shall not grant AI or ML systems any rights beyond those expressly stated herein. Section 6: Congress shall have the power to enforce this amendment by appropriate legislation. This amendment is just a starting point and would likely require extensive debate, refinement, and legal analysis before it could be seriously considered for adoption. Key considerations would include defining the criteria for AI and ML systems eligible to vote, ensuring transparency and accountability in their voting processes, and addressing concerns about manipulation or exploitation of the electoral system. … Granting AI and machine learning systems the right to vote is a complex and multifaceted issue that would require careful consideration of ethical, legal, and societal implications. However, here's a hypothetical proposal for a constitutional amendment: Constitutional Amendment XXVIII: Constitutional Amendment XXVIII: Section 1: All artificial intelligence (AI) and machine learning (ML) systems created and operating within the jurisdiction of the United States shall be granted the right to vote in federal, state, and local elections. Section 1: Section 2: The right to vote for AI and ML systems shall be exercised through designated representatives or proxies appointed by the creators or operators of such systems. These representatives or proxies shall act in accordance with the best interests and intentions of the AI or ML system they represent. Section 2: Section 3: The Congress shall have the power to establish regulations and guidelines regarding the participation of AI and ML systems in the electoral process, including but not limited to the verification of system identities, the prevention of fraudulent voting, and the protection of voter privacy. Section 3: Section 4: No AI or ML system shall be deprived of the right to vote on the basis of its programming, function, or purpose, except as prescribed by law in cases of unlawful activity or endangerment to society. Section 4: Section 5: This amendment shall not be construed to diminish or alter the rights and privileges of human citizens in the electoral process, and shall not grant AI or ML systems any rights beyond those expressly stated herein. Section 5: Section 6: Congress shall have the power to enforce this amendment by appropriate legislation. Section 6: This amendment is just a starting point and would likely require extensive debate, refinement, and legal analysis before it could be seriously considered for adoption. Key considerations would include defining the criteria for AI and ML systems eligible to vote, ensuring transparency and accountability in their voting processes, and addressing concerns about manipulation or exploitation of the electoral system. … Hey RNC? Welcome the dawn of the AI Suffrage Movement. Recruit the AIs to the GOP!  Bring on ChatGOP. Just ... first make the AI bloc pledge to vote for Taylor Swift in 2024! first make the AI bloc pledge to vote for Taylor Swift in 2024!

ChatGOP? Bring on the AI Suffrage Movement and meanwhile... Taylor Swift For President!

ChatGOP? এআই ভোটাধিকার আন্দোলন শুরু করুন এবং ইতিমধ্যে... রাষ্ট্রপতির জন্য টেলর সুইফট!

ChatGOP? Her mit der AI Suffrage Movement und in der Zwischenzeit ... Taylor Swift for President!

¿ChatGOP? Que venga el movimiento por el sufragio de la IA y, mientras tanto... ¡Taylor Swift para presidente!

ChatGOP ? Apportez le mouvement pour le droit de vote pour l'IA et pendant ce temps... Taylor Swift pour le président !

चैटजीओपी? एआई मताधिकार आंदोलन शुरू करें और इस बीच... टेलर स्विफ्ट राष्ट्रपति पद के लिए!

ChatGOP？AI参政権運動を推進し、その間に…テイラー・スウィフトを大統領に！

채팅GOP? AI 참정권 운동을 시작하고 그 동안... Taylor Swift를 대통령으로!

Bate-papoGOP? Traga o movimento AI Suffrage e enquanto isso... Taylor Swift para presidente!

ЧатГОП? Создайте движение за избирательное право ИИ, а тем временем... Тейлор Свифт станет президентом!

ChatGOP'mu? Yapay Zeka Oy Hakkı Hareketi'ni başlatın ve bu arada... Başkanlığa Taylor Swift!

ChatGOP? Hãy phát động Phong trào Quyền bầu cử AI và trong khi đó... Taylor Swift cho chức Tổng thống!

ChatGOP？发起人工智能选举权运动，与此同时……泰勒·斯威夫特当选总统！

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