Nanuis in the Enterprise Collaboration Industry

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VUCA stands for volatility, uncertainty, complexity, and ambiguity. It describes the situation of constant, unpredictable change in the world. Data never sleeps! Actually, data shows no signs of slowing down. In the 9th edition of the , you have a glimpse of how much data is created every digital minute in our increasingly data-driven world. Data Never Sleeps infographic In just 1 minute… Amazon customers spend $283,000 12 million people send an iMessage 6 million people shop online Instacart users spend $67,000 Slack users send 148,000 messages Microsoft Teams connects 100,000 users YouTube users stream 694,000 videos Facebook Live receives 44 million views Instagram users share 65,000 photos TikTok users watch 167 million videos But how this so connected world that changes every single minute affects our way of work, our leadership skills, our companies, our planning, etc.? Well, it affects everything! It has become a trendy managerial acronym: VUCA, short word for olatility, ncertainty, omplexity, and mbiguity. V U C A In 1987 the VUCA acronym was used for the first time in the leadership theories of and , reflecting general conditions and their volatility, uncertainty, complexity, and ambiguity. Warren Bennis Burt Nanus But the term VUCA took place and started to be used in military education, first introduced by the to describe the complex world created as a result of the Cold War. U.S. Army War College According to the , it was in 1991, during the conference “Developing Strategic Leadership: The U.S. Army War College Experience”, defined strategic leadership within a volatile, uncertain, complex, and ambiguous global environment. U.S. Army Heritage and Education Center Herbert F. Barber However, it was just an published in 1992 that attributes the use of the terminology to who characterized the strategic leadership environment in terms of volatility, uncertainty, complexity, and ambiguity. Army War College Study Project General Thurman at Harvard Business Review, says that executives have taken to using the military acronym VUCA–Volatility, Uncertainty, Complexity, Ambiguity–to describe the world in which they operate and to ask that question: In a VUCA world, what’s the point of strategy? Scott Berinato The acronym first coined in the army is now widely applied to business and society. the nature and dynamics of change, and the nature and speed of change forces and change agents. V = Volatility: According to the : vuca-world.org “… We live in a world that’s constantly changing, becoming more unstable each day, where changes big and small are becoming more unpredictable — and they’re getting more and more dramatic and happening faster and faster. As events unfold in completely unexpected ways, it’s becoming impossible to determine cause and effect…” the lack of predictability, the expectation for surprise, and the sense of awareness and understanding of issues and events. U = Uncertainty: According to the : vuca-world.org “… It’s becoming more difficult to anticipate events or predict how they’ll unfold; historical forecasts and past experiences are losing their relevance and are rarely applicable as a basis for predicting the shape of things to come. It’s becoming nearly impossible to plan for investment, development, and growth as it becomes increasingly uncertain where the route is heading….” the multiplex of forces so many sources of issues, no cause-and-effect chain, and confusion that surrounds the organization. C = Complexity: According to the : vuca-world.org “… Our modern world is more complex than ever. What are the reasons? What are the effects? — Problems and their repercussions are more multi-layered, harder to understand. The different layers intermingle, making it impossible to get an overview of how things are related. Decisions are reduced to a tangled mesh of reaction and counter-reaction — and choosing the single correct path is almost impossible….” the nebulous around what is reality, the potential for misreads, and the mixed meanings of conditions that bring cause-and-effect confusion. A = Ambiguity: According to the : vuca-world.org “… Not everything is black and white — grey is also an option. The demands on modern organisations and management are more contradictory and paradoxical than ever, challenging our personal value systems to the core. In a world where the “what” takes a back seat to the “why?” and the “how?”, making decisions requires courage, awareness, and a willingness to make mistakes….” website made a description with fascinating graphics for each one of the 4 elements of VUCA that I recommend you check out . The Living Core here Image: CQ Net (www.ckju.net) The VUCA world should sound like a code of awareness and readiness, saying that the world we are living in does not require just best practices anymore, but best thinking and adaptability. Our approach to problems in a digital and dynamic world should always consider that even a tweet or a picture published on Instagram could affect our strategy or our plan, and it is no longer a case of finding the one (unique) way of working or the single management tool that could solve all the problems like a silver bullet. “… The willingness to engage in genuine cooperation and take on clear responsibilities is a basic prerequisite for innovation. This requires freedom, creativity, speed, flexibility and a corporate culture that connects people with the organization. This connection becomes more significant and can be brought into the focus of leadership even more decisively. In a VUCA world, the most important thing is to anticipate the future and to strengthen cooperation in companies with modern solutions. Decisions and connections are success factors for shaping the common cause…” — The Vuca World Org With too many things to take care of or be concerned about and more coming to our plate every single second, it is easy to feel a bad overload of information and not be able to make smart decisions. This is ironic if you think that in the decade with the biggest amount of data available for humans to make better decisions, we are not being good enough at curating this amount of data and organizing our thoughts. Information overload is the name for the challenge in understanding things and effectively using them, and it is generally associated with the excessive quantity of daily information. When the situation persists, burnout is a common result. Organizations, together with all stakeholders within, are best to approach the reality of volatility, uncertainty, complexity, and ambiguity with open thinking and a strategic approach to responding to the constant changes coming. The VUCA world makes us and our companies concerned about the risk of not being connected and also the risk of being excessively connected and not using the information we gather to make strategic decisions with a wide perspective. Thank you for reading another article here! I hope you enjoyed it! Also Published Here You can support my work in many different ways. You can share it, so it will spread the content to more people. You can also buy me a coffee that will also support my work and maybe give me some extra energy for the next article: Buy me a coffee There are even more good things I’ve prepared for you! Follow me on — LinkedIn Twitter — Instagram

The VUCA World and its Implementation in Leadership Strategy 

Recently, I have decided to become a founder again. After 2.5 years of being employed/contractor, I just felt it was about time to venture out again. There were a couple triggers for that decision, which I will tackle in the following few paragraphs. Firstly, I am grateful for all the people who have put their trust in me in the past 2.5 years and I can honestly say that I gave it my very best every single time. But to be completely transparent, I think a part of me knew that this part of my career was always going to be temporary. I always wanted to be a founder, since I was a kid. It is something deeply embedded in how I think about life. And going forward, I can’t really see myself doing anything else than building companies. Secondly, around December my personal life started falling apart. I went through a really bad break up. And I had one of those epiphany moments when you ask yourself — what the hell am I going to do with my life now. So again, it felt only natural to me to go back to who I really am. And finally, I dug up some of my old ‘Evernotes’ and I realised that I have been passionate about this space for the past 4 years. And I feel with every step that I have taken, I have been subconsciously preparing myself to launch . Crunchdex Before I launched my newest venture, I have decided to write down from all the startups that I have been ever involved in. And because a couple of my close friends have asked me to share this publicly, you can find the full list below. I sincerely hope it will help at least one of you to avoid making the same mistakes that I have made. If it does, please do let me know. It will make my day. the biggest lessons I will keep it bitesized. So feel free to ask for more details in the comments. 1. ClickandStudy 📚 — marketplace of language schoolsLessons: Not all attention will result in revenue.& Don’t ever accept media for equity . Don’t ever do that. I know this might sound like a really harsh opinion, but bare with me for a second. First of all, your shiny new media partner will have absolutely . Second of all, the sheer economics of such a deal never work in the long run. Sure you will see a cool spike in the short term, but at some point there is only as many times as you can recycle a promotional piece of content. And finally, if you are really clever, I am sure you can figure out a way to get in the press anyway. Screw media for equity, seriously. zero skin in the game Secondly, .My first hand experience: We did a viral video together with two Youtube influencers, which now has over . I think only in the first week, it had like 1M views and brought us hundreds of thousands of visitors. Do you know how many conversions we have actually made? De nada. Jackshit. Zero. I mean ok, some people did convert, but none of them became loyal customers. And it had absolutely zero impact on the business. some attention is totally worthless 7M views on Youtube 2. Labelgram 🤳 — influencer marketplaceLessons:**Small Social Media influencers have a bigger influence than celebrities.** If you can’t sell it, don’t even bother building it. . In early 2014, when I started telling brands that it’s better to hire 100 people with 1.000 followers than one celebrity, to promote their newest coolest product, everyone thought I was insane. How would we manage 100 people? Is this even legal? Don’t they have to tell their friends it is product placement? Shouldn’t we rather work with a pro? If you want to run a really effective influencer-driven campaign, hire a lot of small influencers rather than really big ones Ok, in hindsight I have to admit, at that point I had no freaking idea about the legal part. And that’s probably like 50% of why this venture failed. But I still firmly stand behind the claim that you should work with small influencers whose followers actually care about what they post. You wouldn’t believe how many big influencers to grow their follower-base. use bots . I still believe the idea was firm. And if you look at the funded startups in this space, I also think the timing was right. I just didn’t have any experience with enterprise sales at that point of my life. I had a pretty good idea of the influencer/social part. But I should have spent 100% of my time trying to find the first customer before I even started building it. Don’t build it, if you can’t sell it 3. Nestpick 🏠 — Airbnb for long-term rentalsLessons:**If you are a crucial team member, ask for equity as soon as you can.Money can screw up almost any friendship.** So here is another unpopular opinion, I think you should never ever ask someone to sign a non-compete. Especially, if they are a crucial team member. Instead give them equity or stock options and if they want to leave — fine, let them. But they are leaving their baby behind as well. Also, as an early employee . Negotiate. Be reasonable. Get it signed. Get it done. Don’t wait. It will always be a difficult discussion. The longer you wait, the more difficult it will become. don’t ever be afraid to ask for equity M . I hope you will never have to go through this yourself. As experiencing this first hand, especially when you are young can really leave some scars for a long time. There is no real way to avoid this. I think all you can do is try to be honest and sincere. And try to understand other peoples’ motivations. oney can screw up any friendship People will say it’s business. It’s not personal. The thing is — startups are personal, because you start from nothing with a handful of people and a somewhat crazy idea. So when you start making it, try to keep the same attitude as when you had nothing. It’s tough. Most people can’t manage. But if you ever get into a situation where you have to choose $ over your closest friends, try to take a step back and think. Because you can always make more money, but nothing will bring these real friendships back. 4. Rentomato 🧰 — SaaS tool for Landlords and Property ManagersLessons:**Don’t stay in the same industry just because you think it makes sense. Sometimes timing is more important than execution.** After nestpick, I felt it’s only natural to do something in Real Estate again. I can now honestly say it was maybe one of the worst decisions that I have ever made. Why? Simple. Even though, at my job was to rent out as many places as possible, I never actually cared about the real estate part of it. I was happy to market a service that is much needed and that creates a real value for the customer. nestpick However, when we launched Rentomato I quickly realised that this is one of those businesses that you just need to plow through. . Secondly, we wanted to charge a % commission (on rent) instead of a regular SaaS fee. Now, this looks fantastic in a pitch deck. But in reality every commission business which has some real life component (except a very few cases) will have a pretty unscalable operational part. And this part will cost you a fortune to scale. It was extremely difficult for me to get passionate about building a SaaS tool which is a nice-to-have but not an actual painkiller In addition, . In some cases, I think it really defines your fate. So if I now look at successful European startups, I feel we were a bit too early to the party. And given our lack of passion, we just didn’t have the stamina nor the resources to make this business a real success. I truly believe timing really matters for startups prop-tech 5. Spaceship 🚀 — Data Mining Company for B2B and B2CLessons: A bit too many :) was probably the one startup that defined my career so far. If there weren’t for Spaceship, I would have never found my passion for data. I would have never been able to join a VC fund. And I would have never made it anywhere. Spaceship Yes, we really did have Donald Trump with never@president.com on our homepage :D The story of how we founded Spaceship is something that I wanted to share for a long time, because I really find it quite funny… so here it comes.. At some point Rentomato was running out of money pretty fast. One day, I woke up, checked our bank account and found out we have have $7 in the bank. I don’t know why, but my natural reaction was to create a spreadsheet with ideas that could make money immediately. We then ranked every idea on 3 variables — how much it can make, how fast we can build it, and how easily can we market it. The list contained anything from 3D printed weed grinders to webinars. Somehow, we landed on the Spaceship idea. Which for us basically meant to repackage and sell our own lead generation tools. This business then evolved to a data mining ‘agency’ which serviced hundreds of businesses. However, on this journey we had to hike up the price 1500x. We totally oversold the product offering. We unfortunately totally pissed off some valued customers. And we realised that some businesses are good cash-cows, but a bit of a nightmare to grow. If I should however share the biggest learning from this whole Spaceship flight — I would say it’s the following: Now you might be asking; what about passion? Sure, passion is important. But people who are in dire need to make money typically subconsciously evaluate their best skills and go for something that they know how to do fairly well. At least well enough for others to pay for it. I know it doesn’t sound romantic. And sure, most of these businesses will probably not scale. However, and this I firmly believe, it will teach you the most about business. Go and ask every real entrepreneur who had to start from zero at some point of his/her life. Sometimes the best business ideas come from a desperate need to make money. 🛒 🍨 6. DataDaily & Nanuk — Data Marketplace & — marketplace for co-living (for digital nomads)Lessons**:Do things with passion or not at all.Not everyone is meant to be an entrepreneur.** During my free time when I was still in VC, I have helped one of my friends and also my ex-girlfriend to launch an online business. Neither of them was a total failure and both of them made some $. However, it taught me that . And there is absolutely nothing wrong with that. Really. Nobody can tell you how to live your life. And if you don’t want to build a company, it doesn’t mean that your career has less meaning or value. It can actually have a lot more meaning and impact than building a startup. not everyone is cut out to be an entrepreneur Secondly, . If you start obsessing about the startup more than the founder, get the hell out. Just say — sorry, and leave. Immediately. And if you can, on the way out of the door try to politely explain to them that in order to start something real, they need to be really committed. Otherwise, they are wasting the most valuable currency — TIME. Not only theirs. But also the time of everyone involved, which is a crime. don’t ever engage in a halfhearted startup 7. Other Startups 😶 — Lessons: Not that many I have been involved in a few other startups over the past few years. Unfortunately, I cannot say that any of them has taught me anything valuable. And that’s fine as well. Not every experience you go through in your life has to necessarily teach you something. However, once asked me in his podcast — what is the one learning I would tell to my younger self. My answer was: . Simply because you are not learning anything real. Nathan If you ever look back and you don’t feel what you were doing 9, 12, 18 months ago was stupid, then you are seriously wasting your time Podcast If I look back at some of the marketing f*** ups I have made. If I look back at some of the brutally naive sales tactics that I have used. If I look back at some of the terrible business decisions I have made. I FEEL STUPID. Sometimes even a little ashamed. And for me that’s totally fine. This feeling comes from the sheer fact that I am convinced that I have grown and I can do it much better now. That’s awesome. It doesn’t guarantee that next time around it will be a success. But at least I have learned something. So these are some of my lessons so far. I do hope you laughed a little. And I do hope it helps at least one of you at some point of your life. If you enjoyed the read, please ❤ it or even better send me a message.

10 Life Lessons from 8 Startups

I decided to focus full-time in this space towards the end of 2017. Seems like a decade ago now. I think it’s important to note that as an independent creative, I have the luxury to speak freely and I don’t have to tailor my thoughts to appease a venture capital investor or boss. I’m a Bitcoiner. I play with Ethereum too, but mainly because that’s where much of the cryptoArt experimentation has moved to over the past year but I would love to help some more Bitcoin-specific art/music projects get on their feet as well. (I’ve also fiddled with Waves, EOS and Tron but not enough to incorporate their protocols into my work yet.) So, I’ve recapped my contributions and content last year and hope to expand and continue learning more in 2019. Art On The Blockchain Podcast (AOTB) AOTB is the crypto-art and music education- focused podcast brought to you by Cynthia Gayton and myself. They are generally 60–120 minutes long and I produce and edit each episode with all of my own original music and try to keep it in line with the feel from my DJ mixtapes. Hopefully, they come across totally different than what you are used to. Episodes 9–26 were all recorded in 2018. That’s an average of 1.5 podcasts per month of conversations with some of the vanguards of the cryptoArt space. A big bump from our 8 episodes in 2017! Jason Bailey of Artnome.com Cryptograffiti (artist) Jessica Angel (artist) Tatiana Moroz (singer) Dan Anderson of Bitcorns Brian Hoffman of Open Bazaar Jess Houlgrave of Codex Protocol Kev & Jon from Rare Art Labs James Waugh & Trevor of Bidio Ruth Catlow of Furtherfield Sam Hart of Avant.com Poppy Simpson of Meural Grayson Cox, Ronald Gierlach, Brian Gruber & Hollis Witherspoon of Nash Cash Casino Yiying Lu (artist) Joe Chiapetta (artist) Manuel Palou, Moises Sanabria & Eddie Negron of Art404 Travis LeRoy Southworth (artist) Beatriz Ramos and Yehudit Mam of Dada.NYC Matt Hall of Choon Music and Cryptopunks Rob Myers (artist) DJ 3LAU (musician) Mack Flavelle of Cryptokitties Christian Moss of Indie Square Coin Artist of Neon District John Perkins and Jon Craine of Superrare.io Naturel (artist) On top of these long form episodes we also briefly branched out to FM radio with a bi-weekly 30 minute live radio show on WERA in Arlington, Virginia. For this show we focused on news and on local DC-area artists involved in cryptoArt. We had short interviews with artists like Vesa, Coldie and more. Although the show was a lot of fun to to do, we decided to discontinue it as it was hard to gauge how much it was being listened to and we could only repost to the archaic MixCloud website. Sometimes it’s hard being located in the cryptoArt-sparse Washington DC! lol AOTB In The DMV Mixcloud site Crypto-related Music I spent many years making rap music and somehow fell into audio visual memes this year. Here’s a sampling of some of the releases. DJ J-$crilla “ ” ft. Chris DeRose, Peter Schiff Cant Smoke A Bitcoin DJ J-$crilla “ ” I Wear Gucci (My Name’s Craig Wright) MikeInSpace “ ” ft. Theo Goodman (Produced by DJ J-$crilla) Pull The Latch “ ” Worst Podcast Ever Theme Song Junseth’s World uploads Choon Music uploads Ujo Music 2018 A3C Festival in ATL Educator, Speaker and Panelist I found myself looking to educate people more as the conferences and panels in cryptoArt took off after the historic RAREAF Conference at the top of 2018. I participated in a number panels, discussions and even a teaching gig. In 2019, my goal is to continue on this path by educating, learning more and discussing in this niche and exciting space. Here’s a list. Rare Art Festival RAREAF- January 13, 2018 NYC (panelist) Digital Assets- March 31, 2018 WDC (DJ/Speaker) Introduction to Crypto-art and Crypto-music- April 21, 2018 ATL (Speaker/Educator) Rare Digital Art Charity Crypto Auction @ POW DC- May 9, 2018 WDC (Speaker/Host) Creative Tech Week- May 10, NYC (Panelist/DJ) Ethereal Summit- May 11, Brooklyn, NY (Media) Washington Area Lawyers Association Presents DJ J-$crilla & Cynthia Gayton’s “Intro To Cryptocurrencies, CryptoArt and Music, and Blockchain for Creatives”- Sept 17- Oct 2 (Every Monday 7-9pm) WDC (Educator/Presenter) A3C Festival- “How Musicians and Artists Are Using Blockchain” Oct 5, 2018 ATL (Panelist/Speaker) The Rarest Show- Oct 25, 2018 Brooklyn, NY (DJ/Media) Art Decentralized- The World of Distributed Masterpieces- Dec 6, 2018 MIA (Media) a clip from Introduction To CryptoArt & CryptoMusic Writings I’ve always enjoyed writing and have a lofty goal of one day cooking up a novel. I started writing on cryptoArt in 2016 and I added a few more pieces in 2018 to the catalogue. Writing more will be near the top of the list of 2019. Introduction To CryptoArt & CryptoMusic Some NOTES on How To Start Earning Crypto With Music DON’T GO DOWN THAT RABBIT HOLE, SCRILLA “Bitcoin Honey Badger” Acrylic on canvas Crypto- Themed Artwork and Tokenized pieces In 2016/2017 I made a bunch of Counterparty (XCP/BTC) related artwork in the form of Rare Pepes. I made mostly hip-hop and Crypto-themed Pepes. As some in this space know now, I have an opinion- that the Rare Pepe Trading Group ignited this space faster than anything else. In 2018, as cryptoArt exploded, the Ethereum communities got behind the movement and hundreds of platforms seemed to pop up as you can witness in real time at dappradar.com. The Ethereum community is big on NFTs, (I personally dislike the name “NFT” to describe these collectibles but this article is not about that) crypto-collectibles and hackathons. I began experimenting with a few of these platforms but ultimately ended up mostly issuing my Ethereum-based art tokens through SuperRare. I really hope to try out Rare Art Labs more this year as well. I also continued using my go-to Counterparty platform to issue tokens using the Bitcoin blockchain. sprung up around the beginning of February and I’ve really enjoyed that community. Even met my fiancee through the community (Tinder is for boomers.) is one of the most innovative dark corners of the blockchain space and I really enjoy what goes on there. They basically use cryptoArt to break stuff or make you think. I also issued my own, and what I believe to be the first of it’s kind- Proof Of Work tokens to help facilitate my music- meme marketing for “Can’t Smoke A Bitcoin” and “I Wear Gucci.” This tokenizing experiment is certainly an interesting rabbit hole and I look to dive deeper this year! Bitcorns KaleidoscopeXCP Bitcoin Honey Badger (prints soon) Bitcorns.com- (sold out in a day) & (some still available) CNTSMOKECORN CORNTANGCLAN - I WEAR GUCCI (sold 1/1), CANT SMOKE A BITCOIN (sold 4/5), FACES (sold 1/1), CLOCKWORK GREEN (sold 1/1), MUH BAGS (unsold 1/1) SuperRare.io Proof Of Share (XCP Tokens)- and One-of-a-kind tokens that were both airdropped to wallet holders that provably helped promote the release through twitter. They are basically game-ified swag badges using Bitcoin. CANTSMOKEBTC IWEARGUCCI - I really like digitally finger-painting on this unique social media artist site, although I have yet to tokenize any of the pieces on there. dada.nyc - Cool website to monitor stats and artists using XCP Digirare.com Instructional Videos from rarescrilla.com Instructional/Education Videos I am definitely looking to expand more in the video side of things in 2019. Subscribe to my and pages. Youtube BitTube Art On The Blockchain Presents IPFS BATTLE ROYALE Art On The Blockchain Presents BATTLE ROYALE 2: Ethereum vs. Counterparty Blockchain: The Technology That’s Changing The Music Industry Pt. 1 Live at A3C How To Buy A Rare Pepe Using Bitcoin Clip from the “DJPEPE in 60 Seconds” video DJPEPE Maybe the hardest thing to explain to my friends and family is that I am a manager of a Bitcoin frog meme. It all started in 2016- Watch the 60 second video I produced in 2017 . It escalated quickly on January 13, 2018 as I somehow conjured up a 6 foot cardboard cutout to speak on a panel with industry professionals. Love him or hate him, here are some DJPEPE highlights from last year. #memetoo #UCIT here Tokenized cardboard cutout DJPEPE auctioned at RAREAF event calls DJPEPE the first blockchain performance artist Paris Review Rare Art Labs Interview Releases CANT SMOKE A BITCOIN and I WEAR GUCCI weeks ahead of release to DJPEPE token holders. DJPEPE becomes a blockchain farmer Pressed 500 stickers released to the wild. So rare that if you wash it you will destroy it. In fact, you have to pass interview questions in order to even be considered to purchase one. 30 tokenized DAMN shirts Featured in The RAREST BOOK Announced Presidential Candidacy for 2020 became a thing. (Keep it coming!) Fan Art Appearance 1/5 on the HODLCAST this past year. Press Sasha Hodler - Episode , , , & 13 21 30 53 60 Bitsesh- Episode 3 Paris Review The Tatiana Show- & Interview w/ Interview With AOTB J $crilla Rare Art Labs Interview Creative Crypto Magazine Bitsonline- & Artist DJ J $crilla Is Using Tokens to Promote a Music Video Rare Art Festival Canceled Following Crypto Market Crash “Funding Creative Projects with Non-Fungible Tokens” Steemit NewsBTC rarescrilla.com Launched RareScrilla.com _New Breaker Mag shoutout New Art On The Blockchain Epsiode 27 ft. Nanu Berks Another one! New intro too "I WEAR GUCCI…_rarescrilla.com Home page | DJ J Scrilla I am not a website developer but I do my best to keep it active! Hundreds of I’ve produced, 100s of I’ve been a part of, obviously the crypto stuff, my art and my all live on the site. I appreciate all the that link back to it- helps tremendously! songs rap videos mixtapes articles Conclusion I will continue down the rabbit hole I entered in 2014. Bear markets are where the innovation happens and I’m all aboard this . UCIT DLT P.S. There are no experts in this space.

My 2018 CryptoArt/Music ReCap

People often want to assign a hierarchy to art. This need to classify is clearly evident in new and transitional styles like crypto and blockchain art. With so much innovative and unusual work out there, people are ready to nail down some sort of ranking. The thing is, not all art is intended for the same purpose. That makes it hard to compare work side-by-side and then If two artists are trying to evoke different emotions or serve particular purposes, then it’s impossible to compare the pieces to each other. Instead, we should be asking if a piece fulfills the intentions of the artist. hold up a “winner.” If that is done successfully, then the artist has accomplished something. While digital and crypto art may seem different from what the art world is used to, it’s actually following in a long tradition of new and initially disconcerting art. As someone with a background in both the traditional and crypto art spaces, here’s how I think about it: 1. Crypto art falls into three main categories. The first type of art in the crypto space is technology-based. A good example here is Kevin Abosch, who has tokenized his blood in the project incorporating actual into his art. People can literally now buy shares of “him” that are part of his physical being. Another is CoinArtist, who is using blockchain technology to create puzzles and games that have to be decrypted. IAMA COIN, blockchain technology Even though these two artists’ styles are radically different from each other, both CoinArtist and Abosch fall under the umbrella of art that is actually crypto-based and relies on the technology itself to work. The second category of crypto art utilizes blockchain solutions that exist separately from the nature of the artwork itself. For example, an artist in this group may register a physical painting of a vase of flowers on the Blockchain Art Collective — and then via a counterparty bitcoin token. tokenize it The third type of crypto art is where artists use traditional forms, but make the subject matter referential to crypto and blockchain. There is some debate about this category and whether it truly falls under the umbrella of crypto art. I like to think of Warhol, whose work was very focused around fame, pop culture, and money, and who literally painted dollar signs at one point. Of course, it was about money — that’s what it brings to mind for the viewers. And when you see bitcoin in art, it makes you think of cryptocurrency, decentralization, blockchain, and other concepts surrounding the space. In my mind, the content of an artwork, and what it’s speaking to, is also a critical part of what it is when experienced as art. 2. Artists are shaping the space and experimenting. A wide variety of artists out there are doing unique and groundbreaking work. It doesn’t take much to learn about them, either. Here are a few artists to look to in the crypto space: recently made a film about the famous Diego Velásquez painting Las Meninas and is now tokenizing pixelated stills from the film with Snark.Art. Eve Sussman artwork speaks to the imagery of association with blockchain and crypto. Nanu Berks Sites like allow people to co-create digital artwork together and come up with wild, intricate drawings you can collect. DADA , the brainchild of Primavera de Filippi, are physical sculptures that “grow” when people donate bitcoin to pay for the supplies and labor of building them. Plantoids 3. Digital and crypto art is constantly being represented in new ways. So much of the innovation happening is collectively tied up in education. Looking back through different time periods, artists have always thought of new ways to visually represent things and concepts. Picasso, van Gogh, Hilma af Klint — they all pushed boundaries at their times and affected the art environment as we know it today. Sometimes the medium changes. Sometimes the concept is transformed. In other cases, artists adapt the subject matter to their work. The only constant is change, and no matter what comes out of that, the art world is benefitting from something new. New ideas, new styles, new formats — they’re all part of each successive wave when you look back through the history books. The crypto space is experiencing this today. There are so many different phrases people use to speak about an era when new concepts and ideologies were introduced. We’re moving along a line of contemporary art, post-internet art, and now crypto art. This is a new wave of art and artists. It’s exciting we have the resources and ability to get informed and educated about it now, while it’s happening, and to shape what it looks like when we read about crypto art in the timeline of art history in the years to come. Thanks for reading! Our team at the Blockchain Art Collective wants to make sure art world changemakers and innovators — whether individuals or institutions — are having an impact on this growing ecosystem. Sound like you? Fill out the form to apply to the Blockchain Art Collective Working Group.

3 Things To Keep In Mind When Navigating The Crypto Art Landscape

Authors: (1) William P. Wagner IV, Claremont Graduate University. Table of Links Abstract & Introduction Definition of Key Terms Fundamentals of Internet Operation Encapsulation Usage-Based Economic Models Net Neutrality Legal History Researcher Conclusions Areas for Further Exploration & References 3. Fundamentals of Internet Operation 3.1. Hardware Independence. As long as the hardware meets some basic technical requirements to communicate with its neighbors, there are no specific restrictions on hardware or communications medium. The Internet is Hardware Agnostic. All decisions about a given network’s hardware are left to the network administrator or owner to provide the best solution for their application. Copper, fiber, satellite or cellular – that decision is up to the network owner – the Internet does not care. 3.2. Network Independence. As long as the data packets arriving and leaving a network meet some basic technical requirements to communicate with their neighbors, there are no specific restrictions. The Internet does not care who the owner or administrator of a given network is or what topology that network employs – it will route packets based on “Best Effort” routing – the Internet is Network Agnostic. 3.3. Content Independence. As long as the data packets leaving a network meet some basic technical requirements to communicate with their neighbors, they will be prioritized by type not content or source. For example: A home user’s VoIP packet should almost always be more important than an FTP request from a big corporation, except on a subnet of only that company’s FTP servers. The Internet is necessarily Content Agnostic. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

The Agnostic Nature of the Internet: Understanding Hardware, Network, and Content Neutrality

In a landmark paper published March 29, 2024, in the journal, Entropy, a team of visionary researchers from VERSES AI has unveiled a groundbreaking framework that could pave the way for the emergence of a new form of artificial intelligence , operating on the principles of natural intelligence— one that arises from the dense interactions of networked intelligent agents, rather than from a single, monolithic system. based on a first-principles approach to multi-scale artificial intelligence The study, titled “Shared Protentions in Multi-Agent Active Inference,” represents a major milestone in VERSES AI’s ambitious roadmap towards “shared intelligence” and could have profound implications for the future of AI and human-machine cooperation. The Power of Active Inference and Shared Protentions The research, led by Mahault Albarracin and co-authored by Riddhi Jain, Toby St Clere Smithe, Daniel Friedman, Maxwell Ramstead, and renowned neuroscientist Dr. Karl Friston, pioneer of and the , draws upon insights from philosophy, biology, and mathematics to explore how shared goals and collective intelligence can emerge from the interactions of individual intelligent agents. Central to their framework is the concept of “shared protentions” — mutually attuned expectations about future states and actions that enable agents to coordinate their behaviors towards common objectives. Active Inference Free Energy Principle What are "Protentions?" The philosophical foundation of the work lies in the concept of “inner time consciousness” developed by the early 20th-century thinker Edmund Husserl. Husserl proposed that our subjective experience of the present moment always has a certain “temporal thickness” — it encompasses not just the immediate instant, but also traces of the just-past (which he called “retentions”) and anticipations of the near-future (termed “protentions”). The authors of the new study take this idea of an individual’s temporal experience and extend it to the level of a social group. They introduce the notion of “shared protentions” — the idea that members of a group can develop mutually attuned expectations about future states and actions, which then allow them to coordinate their behaviors. How Does it Work? To translate this philosophical concept into a scientific model, the researchers employ a framework from theoretical biology known as “active inference.” This approach views living organisms as continuously making inferences about the state of their environment and selecting actions that minimize surprises and uncertainty. The authors show how Husserl’s ideas can be mapped onto the components of an active inference model. Sensory inputs correspond to raw “hyletic data,” (impressions that form perceptions), while the inferred causes of those sensations make up what we perceive as our experience. Crucially, the model equips each individual with an internal representation that includes not just their own states but also those of other group members. This allows the model to capture how individuals infer each other’s mental states and anticipate their actions. Leveraging Category Theory for Unified Representation The most innovative aspect of the new framework is its integration of mathematical tools from a field known as category theory. The researchers now express how individuals make decisions using abstract math concepts known as “polynomial functors,” which represent their potential interactions and observations. They then use a technique called “sheaf theory” to stitch together the compatible parts of these individual models. This results in a unified picture of the group’s shared understanding, termed a “consensus topos” by the authors. Within this formal framework, the “shared protentions” that enable coordinated group action emerge spontaneously as a consequence of each individual’s understanding and reasoning being influenced by the need to maintain consistency with the group. In other words, the underlying alignment of expectations that allows individuals to work together towards common goals arises naturally from the mathematics of the model. An Example from Nature Think of it like a flock of birds flying together. Each bird is constantly adjusting its own movements based on what it sees the other birds doing. It doesn’t need to have a detailed plan or a leader telling it what to do — it just naturally falls into formation by following simple rules and paying attention to its neighbors. A Human Example - Alignment of Understanding and Anticipation We can also use the example of a sports team, like a basketball team, playing together on the court. Each player on the team has a mental model of the game — they understand the rules, the objectives, and the general strategy. But more importantly, they also have a sense of what their teammates are likely to do in any given situation. This understanding is built up through hours of practice and gameplay together. During a fast-paced game, there’s no time for the players to stop and discuss each play in detail. Instead, they rely on their shared understanding and anticipation of each other’s actions. The point guard, for instance, might make a split-second decision to pass the ball to a particular teammate, not because they discussed it beforehand, but because they anticipate that the teammate will be in the right position to receive the pass. This is a prime example of “shared protentions” in action. Each player’s individual actions are guided by their expectations of what their teammates will do. And these expectations are not just one-way — they’re reciprocal and self-reinforcing. The teammate anticipates the pass because they know the point guard is likely to make it, and the point guard makes the pass because they anticipate the teammate being ready for it. Over time, this interplay of expectations and actions leads to a highly coordinated style of play, where the team seems to move and think as one unit. And importantly, this coordination emerges not from any one player’s explicit instructions, but from the implicit alignment of each player’s understanding and anticipations. This is the essence of what the researchers’ mathematical model captures — It’s a powerful insight into the cognitive underpinnings of teamwork and collective action. how shared goals and coordinated action can arise naturally from the interplay of individual minds, guided by the need to maintain a coherent, mutually aligned understanding of the world. VERSES AI's Vision for Shared Intelligence The significance of this work extends far beyond the realm of academic theory. In a post on LinkedIn, Albarracin explained that the paper “sets the stage for .” This stage, which roughly corresponds to what is popularly known as “Artificial Super-Intelligence,” envisions the emergence of planetary-scale, distributed intelligences from the highly coordinated interactions of networked Intelligent Agents. The key insight of the “Shared Protentions” paper is that such collective intelligences are not just a distant dream but a natural consequence of the principles of Active Inference and the mathematical tools of category theory. VERSES’ Active Inference intelligence roadmap stage S4 of Shared Intelligence The Mathematics of Shared Intelligence To understand how this works, it’s helpful to step back and consider the basic premise of Active Inference. This framework, rooted in neuroscience and theoretical biology, views Intelligent Agents as constantly making deductions and drawing conclusions about the state of their environment and choosing to act in a way that closes the gap between expectation and reality — refining understanding to better anticipate what will happen. In the “Shared Protentions” paper, the authors show how this process of inference and action selection can be formally modeled using abstract mathematical objects called “polynomial functors” and “sheaves,” as explained above. Coordinating Intelligent Agents through Implicit Alignment By applying these tools to networks of interacting Intelligent Agents, the researchers demonstrate how individual agents can intuitively align their expectations and coordinate their behaviors, even without explicit communication. In essence, they provide a mathematical recipe for the unspoken emergence of shared goals and collective intelligence from the bottom up. The Role of HSML in Enabling Shared Intelligence But what makes this research truly groundbreaking is its integration with . HSML is a common language that enables seamless interoperability between Active Inference Intelligent Agents, humans, and the scores of devices that comprise the Internet of Things (IoT). By programming context into objects and spaces, HSML provides Active Inference Agents with real-time data about their environments, as well as the sensory inputs from IoT devices and robots. VERSES AI’s Hyperspace Modeling Language (HSML) Embedding Human Values into the Spatial Web The “Shared Protentions” paper lays the theoretical foundation for In other words, it provides a principled way to embed human values and objectives into the very fabric of the Spatial Web, ensuring that the collective intelligences that emerge from it are aligned with our goals and aspirations. translating high-level human directives, encoded in HSML, into the inherent alignments of expectations that guide the cooperative behaviors of Autonomous Intelligent Agents. The Far-Reaching Implications of VERSES AI's Research The implications of this research are staggering. As we move towards a future in which , from Smart Cities to healthcare to scientific research, the ability to create cooperative, context-aware intelligences that can seamlessly coordinate their actions in pursuit of common goals will be essential. The “Shared Protentions” paper, in conjunction with the — & , offers a roadmap for achieving this vision. Autonomous Intelligent Systems are increasingly integrated into every aspect of our lives Spatial Web Protocol HSTP HSML Challenges and Opportunities on the Path to Shared Intelligence Of course, there are still many challenges to be overcome. This robust, secure, and ethical framework, as well as the deployment of . However, the publication of this paper marks a crucial turning point in our understanding of what is possible. these technologies, will require ongoing collaboration between researchers, policymakers, and the broader public Natural Intelligence-A New Era in Artificial Intelligence As we stand at the threshold of a new era in artificial intelligence, the pioneering work of Friston, Albarracin, and their colleagues at and the offers a promising vision of future in which humans and machines can work together in previously unimaginable ways. By cracking the code of shared intelligence and providing the tools to embed the building blocks for it in the very infrastructure of our world, they are laying the foundation for a new paradigm in AI — one that operates with the same mechanics as biological intelligence, and one that could transform our society in ways we are only beginning to imagine. VERSES AI Spatial Web Foundation , and Visit my blog at https://deniseholt.us/ Subscribe to my Spatial Web AI Podcast on , YouTube , and more. Spotify Subscribe to my newsletter on . LinkedIn Also published . here

VERSES AI's Revolutionary Framework Lays Down The Path to Natural Shared Super-Intelligence

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vogt, Lars, TIB Leibniz Information Centre for Science and Technology; (2) Konrad, Marcel, TIB Leibniz Information Centre for Science and Technology; (3) Prinz, Manuel, TIB Leibniz Information Centre for Science and Technology. Table of Links Abstract & Introduction Interoperability Semantic interoperability and what natural languages like English can teach us Requirements for successfully communicating terms and statements Parallels between the structure of natural language statements and data schemata with implications for semantic interoperability What makes a term a good term and a schema a good schema? The need for a machine-actionable Rosetta Stone for (meta)data that acts as an interlingua for specifying reference terms and reference schemata to support cognitive and semantic interoperability Rosetta Stone and machine-readability: UPRIs, XML Schema datatypes, and RDF for communicating terms, datatypes, and statements Rosetta Stone and machine-interpretability: Wikidata and a modeling paradigm for (meta)data statements based on English Rosetta Stone and semantic interoperability: Specifying term mappings and schema crosswalks Rosetta Stone and cognitive interoperability: Specifying display templates and using a query builder Discussion Related work Conclusion, Acknowledgements, & References Parallels between the structure of natural language statements and data schemata with implications for semantic interoperability Since we can think of each datum as a somewhat formalized representation of a natural language statement, structured in such a way that it can be easily compared with statements of the same type, and easily read and operationalized by machines (cf. Fig. 2A with 2D,E), we can think of a data schema as a formalization of a particular type of natural language statement to support its machine-actionability. In other words, data schemata are to machines what syntax trees are to humans—both define positions with semantic roles. When we compare data schemata with their corresponding natural language statements, we can thus see similarities between the structure of a sentence defined by the syntax and grammar of a natural language and the structure of a corresponding schema (Fig. 2). As discussed above, the of terms in a natural language sentence take on specific and contribute significantly to the meaning of the statement. For a data schema to have the same meaning as its corresponding natural language statements, it must, as a , functionally and semantically provide a similar structure with the same elements as the corresponding syntax tree: the schema must represent all relevant syntactic positions—in schemas often called —and their associated semantic roles in the form of , with terms and values populating the slots (see Fig. 2D,E). After all, humans need to be able to understand these data schemata, and need to be able to translate a given datum represented in a given data schema back into a natural language statement. Data schemata can therefore be seen as attempts to translate the structure of natural language sentences into machine-actionable data structures. syntactic positions semantic roles minimum requirement slots constraint specifications Looking at these interdependencies, we can distinguish different . And since we distinguish between terms and propositions as two different types of meaning-carrying entities when we communicate (meta)data, we can distinguish causes for the lack of terminological interoperability from causes for the lack of propositional interoperability. causes for the lack of semantic interoperability With regard to , we can distinguish between ontological and referential causes for the lack of interoperability of terms (10). When two given terms are compared semantically, they can either (i) differ in both their meaning and their referent (e.g., and ), (ii) differ only in their meaning but share the same referent (e.g., and , both referring to the planet Venus), or (iii) share the same meaning and the same referent. terminological interoperability ‘apple’ ‘car’ ‘Morning Star’ ‘Evening Star’ If two terms share their meaning and their referent, they are They are strict synonyms and can be used interchangeably. Since no two terms can share their meaning but not their referent, ontological interoperability always implies referential interoperability, but not vice versa. Thus, if two terms have the same referent but not the same meaning, because controlled vocabularies may differ in their ontological commitments, their ontological interoperability is violated, but not necessarily their since both terms can be used to refer to the same entity. For example, the COVID-19 Vocabulary Ontology (COVoC) defines ‘viruses’ as a subclass of ‘organism’, while the Virus Infectious Diseases Ontology ( ) defines ‘virus’ as a subclass of ‘acellular structure’, and thus as an object that is not an organism–these two terms are therefore not ontologically interoperable, even though they have the same referent (i.e., the same extension). In other words, the set of ontologically interoperable terms is a subset of the set of referentially interoperable terms. referentially and ontologically interoperable. referential interoperability, vido As far as terminological interoperability is concerned, we can therefore conclude that although ontological interoperability is preferred 2 , , since when we communicate information, we at least want to know that we are referring to the same entities. referential interoperability is the minimum requirement for the interoperability of terms In the context of knowledge graphs and ontologies, if two terms share the same meaning and referent despite having different UPRIs, we can express their terminological interoperability by specifying a corresponding term mapping using the ‘same as’ (owl:sameAs) property. If two terms have only the same referent but not the same meaning, we can express their referential interoperability by specifying a corresponding term mapping using the ‘equivalent class’ (owl:equivalentClass) property. Both types of mappings are (49), where there is only one vocabulary element to be mapped on the left side, and several others on the right side of the definition that do not need to be mapped. We can thus distinguish between ontological (i.e., same-as) and referential (i.e., equivalent-class) term mappings. homogeneous definition mappings With respect to , we can distinguish between logical and schematic causes for the lack of interoperability of statements of the same type (10). Data and metadata statements are if they have been modeled on the basis of the same logical framework (e.g., OWL-Full and thus on full description logics), so that one can reason over them using appropriate reasoners (e.g., an OWL-Full reasoner). When we talk about logically interoperable terms, we are actually referring to the ontological definitions of these terms and thus to their class axioms, which are universal statements that can be placed in the same logical framework if the terms are logically interoperable. propositional interoperability logically interoperable is achieved when statements of the same type are documented using the same (meta)data schema. If statements of the same type were represented using different schemata, corresponding (meta)data would no longer be interoperable. In such cases, one would have to specify schema crosswalks by aligning slots (i.e., syntactic positions) that share the same constraint specification (i.e., semantic roles) across different schemata modeling the same type of statement, in order to regain schematic interoperability (see Fig. 3). If the schemata use different vocabularies to populate their slots (i.e., the constraint specifications refer to different vocabularies), then corresponding term mappings must be included in the crosswalk to ensure terminological interoperability (see red bordered slots in Fig. 3). Consequently, we can distinguish between A schema crosswalk is a set of rules that specifies how (meta)data elements or attributes (i.e., slots) from one schema and format can be aligned and mapped to the equivalent (meta)data elements or attributes in another schema and format. Schematic interoperability ontological and referential schema crosswalks. In other words, to achieve schematic interoperability between two given statements, the subject, predicate, and object slots of their (meta)data schemata need to be aligned, and their terms be mapped across controlled vocabularies. To do this, the schemata must first be formally specified, e.g., in the form of graph patterns specified as SHACL shapes. Shapes that share the same statement-type referent then need to be aligned and mapped. These are (i.e., TBox alignments) (49) or , where several vocabulary elements must first be aligned and then mapped in schema crosswalks. ontology pattern alignments ABox alignments As far as propositional interoperability is concerned, we can therefore conclude that although a combination of logical interoperability and schematic interoperability is preferred, for the same reasons as for terms, schematic interoperability using referential schema crosswalks is the minimum requirement for the interoperability of statements. In summary, the interoperability of (meta)data statements does not only depend on the number of applicable operations and thus on machine-actionability, but also on the completeness of the ontological and referential term mappings and schema crosswalks relevant to the statements.

Parallels Between the Structure of Natural Language Statements and Data Schemata

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vogt, Lars, TIB Leibniz Information Centre for Science and Technology; (2) Konrad, Marcel, TIB Leibniz Information Centre for Science and Technology; (3) Prinz, Manuel, TIB Leibniz Information Centre for Science and Technology. Table of Links Abstract & Introduction Interoperability Semantic interoperability and what natural languages like English can teach us Requirements for successfully communicating terms and statements Parallels between the structure of natural language statements and data schemata with implications for semantic interoperability What makes a term a good term and a schema a good schema? The need for a machine-actionable Rosetta Stone for (meta)data that acts as an interlingua for specifying reference terms and reference schemata to support cognitive and semantic interoperability Rosetta Stone and machine-readability: UPRIs, XML Schema datatypes, and RDF for communicating terms, datatypes, and statements Rosetta Stone and machine-interpretability: Wikidata and a modeling paradigm for (meta)data statements based on English Rosetta Stone and semantic interoperability: Specifying term mappings and schema crosswalks Rosetta Stone and cognitive interoperability: Specifying display templates and using a query builder Discussion Related work Conclusion, Acknowledgements, & References Semantic interoperability and what natural languages like English can teach us The EOSC Interoperability Framework (20) characterizes semantic interoperability as a requirement for enabling machine-actionability between information systems, and it is achieved (p. 11). “when the information transferred has, in its communicated form, all of the meaning required for the receiving system to interpret it correctly” To understand what semantic interoperability means at a conceptual level, it is helpful to consider how we as humans communicate meaning (i.e., semantic content) in a natural language such as English, using terms and statements as the basic units of meaning that carrying information. And when we talk about communication, we mean the attempt to create the same cognitive representation of information in the receiver as is present in the sender.

Semantic Interoperability and What Natural Languages Like English Can Teach us

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 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 [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 [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 [9] Heather Evans. 2016. Do Women Only Talk about ‘Female Issues’? Gender and
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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

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 4.0 license.
Authors:
(1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro
Diverse cross-document coreference and media bias analysis
Annotation tool
Annotation guidelines
Conclusion and future work
Acknowledgements
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Timo Spinde, David Krieger, Manuel Plank, and Bela Gipp. 2021b. Towards a reliable groundtruth for biased language detection. In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 324–325. IEEE. Anastasia Zhukova, Felix Hamborg, Karsten Donnay, and Bela Gipp. 2022. XCoref: Crossdocument coreference resolution in the wild. In International Conference on Information, pages 272–291. Springer. Anastasia Zhukova, Felix Hamborg, and Bela Gipp. 2021. Towards Evaluation of Crossdocument Coreference Resolution Models Using Datasets with Diverse Annotation Schemes. arXiv preprint arXiv:2109.05250. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro Diverse cross-document coreference and media bias analysis Annotation tool Annotation guidelines Conclusion and future work Acknowledgements Bibliographical References Abstract and Intro Abstract and Intro Diverse cross-document coreference and media bias analysis Diverse cross-document coreference and media bias analysis Annotation tool Annotation tool Annotation guidelines Annotation guidelines Conclusion and future work Conclusion and future work Acknowledgements Acknowledgements Bibliographical References Bibliographical References 7. Bibliographical References Ad Fontes Media, Inc. 2023. Media Bias Chart, Version 2.8.4. https://adfontesmedia.com/interactive-mediabias-chart/, accessed 06 Jul 2023. AllSides Technologies Inc. 2023. AllSides Media Bias Chart, Version 9.0. https://www.allsides.com/media-bias/mediabias-chart, accessed 06 Jul 2023. Herbert H. Clark and Adrian Bangerter. 2004. Changing ideas about reference. In Experimental pragmatics, pages 25–49. Springer. Herbert H. Clark and Susan E Haviland. 1977. Comprehension and the Given-New contract. In Discourse Production and Comprehension, pages 1–40. Erlbaum. Felix Hamborg, Anastasia Zhukova, and Bela Gipp. 2019. Automated identification of media bias by word choice and labeling in news articles. In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 196–205. IEEE. Lynette Hirschman and Nancy Chinchor. 1998. Appendix F: MUC-7 coreference task definition (version 3.0). In Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, April 29-May 1, 1998. Jan-Christoph Klie, Michael Bugert, Beto Boullosa, Richard Eckart de Castilho, and Iryna Gurevych. 2018. The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 5–9. Association for Computational Linguistics. Royce Kurmelovs. 2023. Fox News labels Joe Biden a ’wannabe dictator’ during Trump speech. The Guardian. https://www.theguardian.com/media/2023/jun/14/foxnews-labels-joe-biden-a-wannabe-dictatorduring-trump-speech, accessed 13 Sep 2023. Linguistic Data Consortium. 2005. ACE (Automatic Content Extraction) English Annotation Guidelines for Events, Version 5.4.3. https://www.ldc.upenn.edu/sites/www.ldc.upenn .edu/files/english-events-guidelines-v5.4.3.pdf, accessed 22 Sep 2023. Linguistic Data Consortium. 2008. ACE (Automatic Content Extraction) English Annotation Guidelines for Entities, Version 6.6. https://www.ldc.upenn.edu/sites/www.ldc.upenn .edu/files/english-entities-guidelines-v6.6.pdf, accessed 18 Sep 2023. Ruicheng Liu, Rui Mao, Anh Tuan Luu, and Erik Cambria. 2023. A brief survey on recent advances in coreference resolution. Artificial Intelligence Review, pages 1–43. Michael Luciano. 2023. Trump Debuts New Nickname for Biden at New Hampshire Rally. Mediaite. https://www.mediaite.com/politics/trumpdebuts-new-nickname-for-biden-at-newhampshire-rally/, accessed 13 Sep 2023. Anna Nedoluzhko, Jiˇr´ı M´ırovsk`y, and Petr Pajas. 2009. The coding scheme for annotating extended nominal coreference and bridging anaphora in the Prague Dependency Treebank. In Proceedings of the Third Linguistic Annotation Workshop (LAW III), pages 108–111. Tim O’Gorman, Kristin Wright-Bettner, and Martha Palmer. 2016. Richer event description: Integrating event coreference with temporal, causal and bridging annotation. In Proceedings of the 2nd workshop on computing news storylines (CNS 2016), pages 47–56. Marta Recasens, Eduard Hovy, and M. Ant`onia Mart´ı. 2010. A Typology of Near-Identity Relations for Coreference (NIDENT). In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta, Malta. European Language Resources Association (ELRA). Sasha Spala, Nicholas A Miller, Yiming Yang, Franck Dernoncourt, and Carl Dockhorn. 2019. DEFT: A corpus for definition extraction in free-and semi-structured text. In Proceedings of the 13th Linguistic Annotation Workshop, pages 124–131. Timo Spinde, Christina Kreuter, Wolfgang Gaissmaier, Felix Hamborg, Bela Gipp, and Helge Giese. 2021a. Do you think it’s biased? how to ask for the perception of media bias. In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 61–69. IEEE. Timo Spinde, David Krieger, Manuel Plank, and Bela Gipp. 2021b. Towards a reliable groundtruth for biased language detection. In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 324–325. IEEE. Anastasia Zhukova, Felix Hamborg, Karsten Donnay, and Bela Gipp. 2022. XCoref: Crossdocument coreference resolution in the wild. In International Conference on Information, pages 272–291. Springer. Anastasia Zhukova, Felix Hamborg, and Bela Gipp. 2021. Towards Evaluation of Crossdocument Coreference Resolution Models Using Datasets with Diverse Annotation Schemes. arXiv preprint arXiv:2109.05250.

Code Book for Annotation of Diverse Cross-Document Coreference: Conclusion and Future Work

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro
Diverse cross-document coreference and media bias analysis
Annotation tool
Annotation guidelines
Conclusion and future work
Acknowledgements
Bibliographical References Abstract This paper presents a scheme for annotating coreference across news articles, extending beyond traditional identity relations by also considering near-identity and bridging relations. It includes a precise description of how to set up Inception, a respective annotation tool, how to annotate entities in news articles, connect them with diverse coreferential relations, and link them across documents to Wikidata’s global knowledge graph. This multi-layered annotation approach is discussed in the context of the problem of media bias. Our main contribution lies in providing a methodology for creating a diverse cross-document coreference corpus which can be applied to the analysis of media bias by word-choice and labelling. Keywords: coreference resolution, diverse coreference annotation, entity annotation, entity linking, media bias analysis, natural language processing 1. Introduction Coreference is the phenomenon of several expressions in a text all referring to the same person, object, or other entity or event as their referent. Thus, in a narrow sense, analyzing a document with regards to coreference means detecting relations of identity between phrases. The following example (1) illustrates such an identity relation, where coreferential expressions are printed in italics: (1) “Joe Biden arrived in Berlin yesterday, but the president did not come alone.” In (1), the noun phrase “Joe Biden” introduces a new entity while “the president” relates back to that introducing phrase. Within this relation, the introducing phrase “Joe Biden” is called the antecedent while the back-relating phrase “the president” is called an anaphor. Both expressions are coreferential in the way that they refer to the same non-textual entity, namely to the actual ‘realworld’ Joe Biden or at least to a corresponding mental concept. We can think of an antecedent and its anaphora as forming a cluster of mentions that as a whole represents its extra-textual referent within a textual document, as shown in Figure 1. As a task of natural language processing (NLP), coreference resolution has become quite efficient in detecting identity relations between phrases. However, reflecting on how we use language to refer to something, we are forced to realize that coreference in a broader sense is actually far more complex. We can address an entity or event by using a variety of expressions that are in fact not strictly identical to each other. Consider the following examples: (2) “President Biden was clearly not satisfied with today’s outcome. As the White House stated this afternoon, efforts will be made to . . . ” (3) “Even if the young Erdogan used to be pro-Western, Turkey’s president nowadays often acts against Western interests.” (4) “The AfD is circulating a photo of Angela Merkel with a Hijab, although Merkel never wore Muslim clothes.” In these given examples, the highlighted mentions mean ‘almost’ the same, but not completely. In (2), we are aware by world-knowledge that ”the White House” is often used as a substitute expression for the current US president, although the former is a place which in strict terms cannot be identical to the president, who is a person. In (3), on the other hand, both mentions refer to the ’real-world’ person Erdogan, but at different time steps. Finally, in (4), a mention representing the person Merkel is juxtaposed with a mention representing a picture of Merkel. While these two mentions could refer to separate entities, the juxtaposition indicates a connection between both where the attributes of the first mention do influence the perception of the second mention. Hence, we would miss essential semantic connections if we chose not to mark them as coreferential. Having said that, the simple classification of two mentions into coreferential (identical) or noncoreferential (non-identical) does not seem to suffice the complexity of common text data. Instead, we need to allow for diverse coreference clusters that include finer-grained relations lying between identity and non-identity. We need to allow for near-identity relations to mark two mentions that are partially, but not totally, identical (Recasens et al., 2010). In news coverage, identity and near-identity references are extensively used to report on persons, organizations, and other entities of public interest. It is our goal to build up a corpus that contains annotated examples of such diverse forms of coreference. While diverse coreference occurs in all sorts of news media, we focus on digital print media, only. Furthermore, although in practice both entities and events can act as referent, we ignore references to events for now, as their annotation would go beyond the limits of our present scheme.[1] The ordinary business of journalism is to write about current political affairs and other happenings of public interest. These happenings are normally reported by several newspapers at the same time. All of these news articles are considered documents that contain references to the same entities and together form a discourse about them. To include the whole picture of such intradiscursive references, we want our corpus to link document-level clusters with corresponding clusters of other documents of the same discourse. Hence, our corpus is to depict cross-document coreference data. On a discourse level, corresponding clusters form discourse entities that themselves can be linked to their non-textual referents by some knowledge graph identifier. For this project, we use Wikidata’s Uniform Resource Identifiers (URIs) for entity linking. By doing so, world knowledge is included into the data. This allows for drawing connections even between different discourse entities that refer to a common referent, yet at a different time step or rather in the context of a different happening. Figure 2 illustrates the multiple layers of this annotation model. In building a corpus for diverse cross-document coreference in news articles, we hope to provide a valuable resource for the evaluation of automated coreference resolution tasks. The contribution of this paper mainly lies in providing an answer to the question of how to create such a corpus. How can diverse coreference relations be annotated in a cross-document setup? We believe our scheme as we present it here tackles this problem efficiently, extensively, and unambiguously. Additionally, we would like to use the data resulting from our own annotations for further research in the area of media bias. Even if it plays no direct part in the outlined scheme, a lot of our choices how to annotate references were made because of this requirement to make the data usable for later media bias analysis. Eventually, we hope to contribute to the wider research question of how to identify media bias by word choice and labelling based on the usage of diverse coreference relations in news articles. The following section 2 will further elaborate on this connection between diverse coreference and the problem of media bias analysis. Despite its only subtle impact on our practical annotation instructions, that section means to highlight the theoretical background and motivation behind our project. The sections thereafter will then deal with the actual annotation process. Section 3 will guide coders through the setup and controls of Inception, our selected annotation software. Finally, section 4 will define annotation instructions in three passes while also outlining our typology of diverse coreference. The data we use for our own annotations consists of the text bodies of articles that report on the same happenings. All articles are in English and were published by one of the following US-American newspapers: HuffPost (categorized as ”Left” by AllSides (2023) or ”Skews Left” by Ad Fontes Media (2023), abbreviated in our data as ”LL”), The New York Times (categorized as ”Lean Left” by AllSides or ”Skews Left” by Ad Fontes Media, abbreviated as ”L”), USA Today (categorized as ”Lean Left” or ”Middle or Balanced Bias”, abbreviated as ”M”), Fox News (categorized as ”Right” or ”Skews Right”, abbreviated as ”R”), Breitbart News Network (categoized as ”Right” or ”Strong Right”, abbreviated as ”RR”).[2] [1] Though at a later point, this scheme could be extended to also include the annotation of events (Linguistic Data Consortium, 2005; O’Gorman et al., 2016). [2] Looking at the political orientation of these newspapers, the data is unbalanced with an underrepresentation of politically centered media. However, at the current state of this project, the imbalance is unlikely to influence our analysis which does not yet target political orientation or media bias itself. Therefore, we will ignore this issue for now. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro Diverse cross-document coreference and media bias analysis Annotation tool Annotation guidelines Conclusion and future work Acknowledgements Bibliographical References Abstract and Intro Abstract and Intro Diverse cross-document coreference and media bias analysis Diverse cross-document coreference and media bias analysis Annotation tool Annotation tool Annotation guidelines Annotation guidelines Conclusion and future work Conclusion and future work Acknowledgements Acknowledgements Bibliographical References Bibliographical References Abstract This paper presents a scheme for annotating coreference across news articles, extending beyond traditional identity relations by also considering near-identity and bridging relations. It includes a precise description of how to set up Inception, a respective annotation tool, how to annotate entities in news articles, connect them with diverse coreferential relations, and link them across documents to Wikidata’s global knowledge graph. This multi-layered annotation approach is discussed in the context of the problem of media bias. Our main contribution lies in providing a methodology for creating a diverse cross-document coreference corpus which can be applied to the analysis of media bias by word-choice and labelling. Keywords : coreference resolution, diverse coreference annotation, entity annotation, entity linking, media bias analysis, natural language processing Keywords 1. Introduction Coreference is the phenomenon of several expressions in a text all referring to the same person, object, or other entity or event as their referent. Thus, in a narrow sense, analyzing a document with regards to coreference means detecting relations of identity between phrases. The following example (1) illustrates such an identity relation, where coreferential expressions are printed in italics: (1) “Joe Biden arrived in Berlin yesterday, but the president did not come alone.” In (1), the noun phrase “Joe Biden” introduces a new entity while “the president” relates back to that introducing phrase. Within this relation, the introducing phrase “Joe Biden” is called the antecedent while the back-relating phrase “the president” is called an anaphor. Both expressions are coreferential in the way that they refer to the same non-textual entity, namely to the actual ‘realworld’ Joe Biden or at least to a corresponding mental concept. We can think of an antecedent and its anaphora as forming a cluster of mentions that as a whole represents its extra-textual referent within a textual document, as shown in Figure 1. As a task of natural language processing (NLP), coreference resolution has become quite efficient in detecting identity relations between phrases. However, reflecting on how we use language to refer to something, we are forced to realize that coreference in a broader sense is actually far more complex. We can address an entity or event by using a variety of expressions that are in fact not strictly identical to each other. Consider the following examples: (2) “ President Biden was clearly not satisfied with today’s outcome. As the White House stated this afternoon, efforts will be made to . . . ” President Biden the White House (3) “Even if the young Erdogan used to be pro-Western, Turkey’s president nowadays often acts against Western interests.” the young Erdogan Turkey’s president (4) “The AfD is circulating a photo of Angela Merkel with a Hijab , although Merkel never wore Muslim clothes.” a photo of Angela Merkel with a Hijab In these given examples, the highlighted mentions mean ‘almost’ the same, but not completely. In (2), we are aware by world-knowledge that ” the White House ” is often used as a substitute expression for the current US president, although the former is a place which in strict terms cannot be identical to the president, who is a person. In (3), on the other hand, both mentions refer to the ’real-world’ person Erdogan, but at different time steps. Finally, in (4), a mention representing the person Merkel is juxtaposed with a mention representing a picture of Merkel. While these two mentions could refer to separate entities, the juxtaposition indicates a connection between both where the attributes of the first mention do influence the perception of the second mention. Hence, we would miss essential semantic connections if we chose not to mark them as coreferential. Having said that, the simple classification of two mentions into coreferential (identical) or noncoreferential (non-identical) does not seem to suffice the complexity of common text data. Instead, we need to allow for diverse coreference clusters that include finer-grained relations lying between identity and non-identity. We need to allow for near-identity relations to mark two mentions that are partially, but not totally, identical (Recasens et al., 2010). the White House diverse coreference near-identity In news coverage, identity and near-identity references are extensively used to report on persons, organizations, and other entities of public interest. It is our goal to build up a corpus that contains annotated examples of such diverse forms of coreference. While diverse coreference occurs in all sorts of news media, we focus on digital print media, only. Furthermore, although in practice both entities and events can act as referent, we ignore references to events for now, as their annotation would go beyond the limits of our present scheme.[1] The ordinary business of journalism is to write about current political affairs and other happenings of public interest. These happenings are normally reported by several newspapers at the same time. All of these news articles are considered documents that contain references to the same entities and together form a discourse about them. To include the whole picture of such intradiscursive references, we want our corpus to link document-level clusters with corresponding clusters of other documents of the same discourse. Hence, our corpus is to depict cross-document coreference data. On a discourse level, corresponding clusters form discourse entities that themselves can be linked to their non-textual referents by some knowledge graph identifier. For this project, we use Wikidata’s Uniform Resource Identifiers (URIs) for entity linking. By doing so, world knowledge is included into the data. This allows for drawing connections even between different discourse entities that refer to a common referent, yet at a different time step or rather in the context of a different happening. Figure 2 illustrates the multiple layers of this annotation model. In building a corpus for diverse cross-document coreference in news articles, we hope to provide a valuable resource for the evaluation of automated coreference resolution tasks. The contribution of this paper mainly lies in providing an answer to the question of how to create such a corpus. How can diverse coreference relations be annotated in a cross-document setup? We believe our scheme as we present it here tackles this problem efficiently, extensively, and unambiguously. Additionally, we would like to use the data resulting from our own annotations for further research in the area of media bias. Even if it plays no direct part in the outlined scheme, a lot of our choices how to annotate references were made because of this requirement to make the data usable for later media bias analysis. Eventually, we hope to contribute to the wider research question of how to identify media bias by word choice and labelling based on the usage of diverse coreference relations in news articles. The following section 2 will further elaborate on this connection between diverse coreference and the problem of media bias analysis. Despite its only subtle impact on our practical annotation instructions, that section means to highlight the theoretical background and motivation behind our project. The sections thereafter will then deal with the actual annotation process. Section 3 will guide coders through the setup and controls of Inception, our selected annotation software. Finally, section 4 will define annotation instructions in three passes while also outlining our typology of diverse coreference. The data we use for our own annotations consists of the text bodies of articles that report on the same happenings. All articles are in English and were published by one of the following US-American newspapers: HuffPost (categorized as ”Left” by AllSides (2023) or ”Skews Left” by Ad Fontes Media (2023), abbreviated in our data as ”LL”), The New York Times (categorized as ”Lean Left” by AllSides or ”Skews Left” by Ad Fontes Media, abbreviated as ”L”), USA Today (categorized as ”Lean Left” or ”Middle or Balanced Bias”, abbreviated as ”M”), Fox News (categorized as ”Right” or ”Skews Right”, abbreviated as ”R”), Breitbart News Network (categoized as ”Right” or ”Strong Right”, abbreviated as ”RR”).[2] [1] Though at a later point, this scheme could be extended to also include the annotation of events (Linguistic Data Consortium, 2005; O’Gorman et al., 2016). [2] Looking at the political orientation of these newspapers, the data is unbalanced with an underrepresentation of politically centered media. However, at the current state of this project, the imbalance is unlikely to influence our analysis which does not yet target political orientation or media bias itself. Therefore, we will ignore this issue for now.

Code Book for Annotation of Diverse Cross-Document Coreference: Abstract and Intro

Examining Social Network Design In The Context Of Web3

Supra, a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains announced its integration with ApeChain. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. GameFi, DeFi, and other decentralised applications on ApeChain will benefit from improved real-time data accessibility, enhanced functionality, and expedited decision-making thanks to Supra's oracle price feeds protocol, the Distributed Oracle Agreement (DORA). Supra's decentralised Verifiable Random Function (dVRF) will also produce verifiable and decentralised randomness for ApeChain dApps, guaranteeing the integrity of random results in prize draws, blockchain games, and other situations where complete transparency is necessary to uphold confidence.Supra is excited to announce its integration with ApeChain. Supra is a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. Delivering Essential Data to dApps Supra's novel oracle data feeds significantly improve the possibilities of smart contracts by offering safe access to outside data that Web3 dApps require. With more than 475 active price feeds and counting, DORA is built to effectively validate and transmit real-world data to deterministic blockchain networks. Delivering comprehensive security guarantees together with high-quality price feeds is ensured by its resilience to Byzantine corruption, protection against potential node collusion, and Layer-1 grade security. Supra accommodates the various requirements of dApps by supporting both push and pull models. Supra provides robust security assurances and remarkable performance speeds for its data streams, all powered by the Supra Moonshot Consensus protocol. The Moonshot Consensus system, with 500 millisecond optimistic finality and roughly 530K transactions per second throughput, was tested extensively throughout the world. Ensuring Trust with Verifiable On-Chain Randomness Supra's dVRF protocol, which builds on DORA's advantages, uses decentralised, verifiable randomness to meet the requirement for unpredictable outcomes in dApps. For Web3 game developers, who need to ensure the integrity and unpredictable nature of random results in situations such as competition matchups and prize draws, this is essential. By enabling dApps to produce easily verifiable random outcomes, Supra's on-chain verifiable randomness generator contributes to increased community trust. ApeChain: Setting New Standards for GameFi Built on Arbitrum, ApeChain is a specialised Layer 3 solution intended to serve as the cornerstone of the Ape community and foster the expansion of ApeCoin. Located where NFTs and gaming converge, ApeChain's specialised infrastructure will support many new games and protocols inside the ApeCoin ecosystem, guaranteeing a seamless and effective gaming experience. GameFi and metaverse decentralised apps have shown a great deal of interest in ApeChain, which has raised the need for trustworthy data oracles that offer up-to-date off-chain data. To guarantee fairness, gaming apps also need verifiable on-chain randomness. Game developers using ApeChain may create more immersive experiences with improved performance by utilising Supra's dVRF and oracle data feeds. “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” — Hervé Larren, ApeCoin DAO Special Council and co-author of ApeChain. ApeChain wants to speed up the widespread use of GameFi by collaborating with Supra to provide more dependable data and foster trust through outcomes that are assured to be verifiably fair. What Is Supra Supra is a top vendor of verifiable randomness services and oracle price feeds across more than 80 blockchains. Supra works as an vertically integrated L1, offering robust security guarantees along with high-quality, low-latency data and randomness across Web 3. It is compatible with MoveVM chains, Bitcoin L2s, EVM L1s, L2s, and L3s dApps. With an emphasis on scalability, security, and quick transactions, Supra offers a full developer toolkit along with in-depth manuals and technical whitepapers. Accompany Supra on its journey to see its expansion, creativity, and engagement with the community. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Supra , a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains announced its integration with ApeChain. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. Supra GameFi, DeFi, and other decentralised applications on ApeChain will benefit from improved real-time data accessibility, enhanced functionality, and expedited decision-making thanks to Supra's oracle price feeds protocol, the Distributed Oracle Agreement (DORA). Supra's decentralised Verifiable Random Function (dVRF) will also produce verifiable and decentralised randomness for ApeChain dApps, guaranteeing the integrity of random results in prize draws, blockchain games, and other situations where complete transparency is necessary to uphold confidence.Supra is excited to announce its integration with ApeChain. Supra is a leading provider of verifiable randomness services and oracle data feeds across over 80 blockchains. As a result, Supra is the first protocol to use verifiable randomness and oracle price feeds on the new Layer 3 on Arbitrum. ApeChain ApeChain Delivering Essential Data to dApps Supra's novel oracle data feeds significantly improve the possibilities of smart contracts by offering safe access to outside data that Web3 dApps require. With more than 475 active price feeds and counting, DORA is built to effectively validate and transmit real-world data to deterministic blockchain networks. Delivering comprehensive security guarantees together with high-quality price feeds is ensured by its resilience to Byzantine corruption, protection against potential node collusion, and Layer-1 grade security. Supra accommodates the various requirements of dApps by supporting both push and pull models. Supra provides robust security assurances and remarkable performance speeds for its data streams, all powered by the Supra Moonshot Consensus protocol. The Moonshot Consensus system, with 500 millisecond optimistic finality and roughly 530K transactions per second throughput, was tested extensively throughout the world. Ensuring Trust with Verifiable On-Chain Randomness Supra's dVRF protocol, which builds on DORA's advantages, uses decentralised, verifiable randomness to meet the requirement for unpredictable outcomes in dApps. For Web3 game developers, who need to ensure the integrity and unpredictable nature of random results in situations such as competition matchups and prize draws, this is essential. By enabling dApps to produce easily verifiable random outcomes, Supra's on-chain verifiable randomness generator contributes to increased community trust. ApeChain: Setting New Standards for GameFi Built on Arbitrum, ApeChain is a specialised Layer 3 solution intended to serve as the cornerstone of the Ape community and foster the expansion of ApeCoin. Located where NFTs and gaming converge, ApeChain's specialised infrastructure will support many new games and protocols inside the ApeCoin ecosystem, guaranteeing a seamless and effective gaming experience. GameFi and metaverse decentralised apps have shown a great deal of interest in ApeChain, which has raised the need for trustworthy data oracles that offer up-to-date off-chain data. To guarantee fairness, gaming apps also need verifiable on-chain randomness. Game developers using ApeChain may create more immersive experiences with improved performance by utilising Supra's dVRF and oracle data feeds. “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” “Supra is pioneering solutions with integrations across all major ecosystems and will now empower ApeChain through Bridges, Oracles, Automation, and Randomizers. The ApeCoin DAO eagerly anticipates the innovative creations that builders will craft, leveraging Supra’s best-in-class stack for games, DeFi, and beyond!” — Hervé Larren, ApeCoin DAO Special Council and co-author of ApeChain. ApeChain wants to speed up the widespread use of GameFi by collaborating with Supra to provide more dependable data and foster trust through outcomes that are assured to be verifiably fair. What Is Supra Supra is a top vendor of verifiable randomness services and oracle price feeds across more than 80 blockchains. Supra works as an Supra Supra vertically integrated L1, offering robust security guarantees along with high-quality, low-latency data and randomness across Web 3. It is compatible with MoveVM chains, Bitcoin L2s, EVM L1s, L2s, and L3s dApps. With an emphasis on scalability, security, and quick transactions, Supra offers a full developer toolkit along with in-depth manuals and technical whitepapers . Accompany Supra on its journey to see its expansion, creativity, and engagement with the community. in-depth manuals in-depth manuals technical whitepapers technical whitepapers Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program

Supra Integrates First Ever Oracle Data Feed and Verifiable Randomness on ApeChain

ApeChain-এ সুপ্রা প্রথম ওরাকল ডেটা ফিড এবং যাচাইযোগ্য এলোমেলোতা একীভূত করে

Supra integriert den ersten Oracle-Datenfeed und verifizierbare Zufälligkeit auf ApeChain

Supra integra la primera fuente de datos de Oracle y aleatoriedad verificable en ApeChain

Supra intègre le tout premier flux de données Oracle et le caractère aléatoire vérifiable sur ApeChain

सुप्रा ने एपचेन पर पहली बार ऑरेकल डेटा फीड और सत्यापन योग्य रैंडमनेस को एकीकृत किया

Supra、ApeChainで初のOracleデータフィードと検証可能なランダム性を統合

Supra는 ApeChain에 최초의 Oracle 데이터 피드와 검증 가능한 무작위성을 통합합니다.

Supra integra o primeiro feed de dados Oracle e aleatoriedade verificável no ApeChain

Supra интегрирует первый в мире поток данных Oracle и проверяемую случайность в ApeChain

Supra, ApeChain'e İlk Oracle Veri Beslemesini ve Doğrulanabilir Rastgeleliği Entegre Ediyor

Supra tích hợp nguồn cấp dữ liệu Oracle đầu tiên và tính ngẫu nhiên có thể xác minh được trên ApeChain

Supra 在 ApeChain 上集成首个 Oracle 数据源和可验证随机性

Few days ago, Mira Murati, Chief Technology Officer at OpenAI, introduced the company's latest GPT model - GPT-4o. A new flagship model set to change how people interact with AI. https://youtu.be/wiFNXirN9WQ?si=wbVDa23tq0i7B24s&embedable=true Visit my YouTube Channel What is GPT-4o? GPT-4o is a new model from OpenAI that can understand and generate text, audio, and images quickly and accurately. It works well in many languages and is cheaper and faster than older models. GPT-4o combines all these functions into one system, making interactions with computers more natural and efficient. It also has safety features built in. Top Highlights of GPT-4o Free Access OpenAI wants everyone to have access to advanced AI tools. With the release of GPT-4o, everyone, even those using the free version, can use GPT-4-level intelligence. This effort is meant to remove obstacles and make AI available to more people. Desktop Version of ChatGPT The new desktop version of ChatGPT is designed to be simpler and easier to use. The updated interface makes it easy to fit into users' daily tasks, improving how user-friendly and accessible it is. Advanced Voice Mode GPT-4o has a new voice mode that makes conversations feel more natural. It quickly handles speech, turning it into text and responding with speech, all with very little delay. This creates a smooth and lifelike interaction for users. Enhanced Vision Capabilities GPT-4o has a new voice mode that can handle real-time conversations. It also has vision capabilities, allowing users to upload and discuss pictures, screenshots, and documents. This makes ChatGPT more versatile and useful in different ways. Multilingual Support Supporting 50 different languages. This is a crucial step in making AI accessible globally regardless of language preference. How to Access GPT-4o for Free To access GPT-4o, follow these steps: Visit chatgpt.com and register for an account.


After logging in, go to the top left corner of the page.





Select the desired model from the available options. If you enjoyed this article, please like and share your feedback. Don't forget to subscribe for more content, and share any topics you'd like me to cover in future articles. Cheers! :) Few days ago, Mira Murati, Chief Technology Officer at OpenAI, introduced the company's latest GPT model - GPT-4o. A new flagship model set to change how people interact with AI. https://youtu.be/wiFNXirN9WQ?si=wbVDa23tq0i7B24s&embedable=true https://youtu.be/wiFNXirN9WQ?si=wbVDa23tq0i7B24s&embedable=true Visit my YouTube Channel Visit my YouTube Channel Visit my YouTube Channel What is GPT-4o? GPT-4o is a new model from OpenAI that can understand and generate text, audio, and images quickly and accurately. It works well in many languages and is cheaper and faster than older models. GPT-4o combines all these functions into one system, making interactions with computers more natural and efficient. It also has safety features built in. Top Highlights of GPT-4o Free Access OpenAI wants everyone to have access to advanced AI tools. With the release of GPT-4o, everyone, even those using the free version, can use GPT-4-level intelligence. This effort is meant to remove obstacles and make AI available to more people. Desktop Version of ChatGPT The new desktop version of ChatGPT is designed to be simpler and easier to use. The updated interface makes it easy to fit into users' daily tasks, improving how user-friendly and accessible it is. Advanced Voice Mode GPT-4o has a new voice mode that makes conversations feel more natural. It quickly handles speech, turning it into text and responding with speech, all with very little delay. This creates a smooth and lifelike interaction for users. Enhanced Vision Capabilities GPT-4o has a new voice mode that can handle real-time conversations. It also has vision capabilities, allowing users to upload and discuss pictures, screenshots, and documents. This makes ChatGPT more versatile and useful in different ways. Multilingual Support Supporting 50 different languages. This is a crucial step in making AI accessible globally regardless of language preference. How to Access GPT-4o for Free To access GPT-4o, follow these steps: Visit chatgpt.com and register for an account. After logging in, go to the top left corner of the page. Select the desired model from the available options. Visit chatgpt.com and register for an account. Visit chatgpt.com and register for an account. chatgpt.com After logging in, go to the top left corner of the page. After logging in, go to the top left corner of the page. Select the desired model from the available options. Select the desired model from the available options. If you enjoyed this article, please like and share your feedback. Don't forget to subscribe for more content, and share any topics you'd like me to cover in future articles. Cheers! :)

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