HAMMOND, Ind. (AP) — A man who led officers in northwestern Indiana on a chase died after crashing a stolen U-Haul truck into a river, police said.

Man driving stolen U-Haul and fleeing cops dies after crashing into river - Indianapolis News | Indiana Weather | Indiana Traffic | WISH-TV |

The seven-year-old girl who became trapped in a sand hole with her brother while they were digging at a Florida beach Tuesday was visiting the state from Indiana with her family.

Girl who died in Florida sand hole while digging with her brother at beach identified as 7-year-old from Indiana

According to a public session notice, the Indiana Election Commission will convene for a public meeting at 10 a.m. on Feb. 27 to discuss the various challenges brought forward in this primary. The meeting will be hosted at the Indiana Government Center South, located at 402 W. Washington Street ...

Indiana Election Commission to host public meeting on 2024 candidate challenges

The sheriff&#x27;s office said the siblings were on vacation with their parents in South Florida from Indiana. A preliminary investigation revealed the children were digging a five to six-foot hole in the sand when it collapsed and they became trapped. (CBS Newspath: WFOR)

New details released about Indiana siblings' deadly beach incident in South Florida

A public information officer from the department said investigators believed that the two children were on vacation from Indiana with their family. While on the beach, the kids dug a hole in the sand and soon became trapped.

7-year-old Indiana girl dies after getting trapped in sand at Florida beach - Indianapolis News | Indiana Weather | Indiana Traffic | WISH-TV |

MUNCIE, Ind. (WXIN) – An Indiana mother has been charged with reckless homicide after her child died in her bed after she fell asleep while breastfeeding in October 2023. This comes after a similar incident occurred with another one of her children in 2020.

Indiana mother charged after infant dies during co-sleeping

BROWARD COUNTY, Florida — A 7-year-old Fort Wayne, Indiana girl has died after being found trapped under sand in Broward County, Florida on Tuesday.

7-year-old Indiana girl dies after being found trapped under sand in Florida

The 2024 Indiana boys high school basketball playoffs are here as teams throughout the Hoosier State work to make it to the state championships on Saturday, March 30, at Gainbridge Fieldhouse.

Indiana boys high school basketball playoffs brackets: 2024 IHSAA matchups, game times - Sports Illustrated High School News, Analysis and More

In Indiana, almost 4 million people received their primary series (a little over 60% of Indiana&#x27;s population), but less than a million people are currently up-to-date (only 17% of the population), according to the Indiana Department of Health.

More than 1 in 4 people in Indiana with COVID have long term symptoms, Census data show

INDIANAPOLIS (AP) — Indiana lawmakers voted to lift a nearly 40-year ban on happy hours Tuesday.

Indiana lawmakers support lifting state ban on happy hours | News | wlfi.com

IndiaNIC

indianic

Which is the best Chatbot development company in India & the United States? This is the most confusing question for modern developers and business owners.  But, not all chatbot companies are fully perfect in terms of quality.This is what creates an uproar among people to find the best when Chatbot is already a hot topic among us. Taking this into account, I have pre-selected some chatbot companies after analyzing several parameters namely: Diversified portfolio Previous work experience Technical experience Strictly NDA agreement Years of experience 100% Money back guarantee The list provided below of chatbot companies will surely help you find the right technology partner for all your Chatbot development needs. Additionally, several references have been taken from companies that evaluate companies that name Good companies, Clutch, etc. to provide you with the best collection of chatbot companies.  This will not only save time, but also your manual efforts. Here is a summarized list of chatbot development companies: Top in the list of chatbot companies. Develop custom chatbots that cater to business needs. 1. ValueCoders : Build ai chatbots that can communicate with customers on all channels. 2. CedexTechnologies : Stand on the third position in the list of best chatbot companies. 3. PixelCrayons : On of the Leading chatbot companies in India, improving position in the list. 4. E2logy : Offer companies to help develop chatbots from ideation to integration. 5. OnGraph Technologies Corporation : Reputed name in the list of chatbot companies by delivering top-notch ai chatbots messengers to the clients. 6. Affle Enterprise : Offers self-evolving, intuitive and engaging bot solutions. 7. Enterpi Software Solutions : Serves top-notch chatbot development services to clients across various industries. 8. IndiaNIC Infotech Ltd : One of the trusted chatbot companies in India. 9. Chapter247 Infotech : A considerable choice among the best chatbot companies in India. 10. ELEKS : Let's discuss these Chatbot development companies in detail: 1) ValueCoders (One of the best chatbot companies in India) Location: India/USA Pricing: Starting from $12/hr Employees: 700+ Established: 2004 Chatbots of the new era is creating a different way of working. ValueCoders is one of top which offers a full cycle of chatbot development services to revolutionize the way businesses interact with their clients and streamline processes. software development companies in India The company has above 14-years of experience and 450+ developers. It has worked for SMEs, startups, digital agencies, and the same type companies. It has 2500+ satisfied clients, and its score is 96 out of 100 for the client retention rate. Therefore, ValueCoders turns out to be one of the ISO and CMMI Level 3 certified Chatbot development firms. You can hire top chatbot developers from ValueCoders and can save up to 60% of development cost. They provide highly sophisticated and intelligent chatbot solutions that can be utilized in various domains such as healthcare sector, entertainment, customer support, e-commerce, and supply chain. 2) Cedex Technologies LLP (One of the best chatbot development agencies in India) Location: Cochin, India Pricing: < $25 / hr Employees: 10 - 49 Established: 2015 With so many chatbot companies, Cedex Technologies LLP is a young and dynamic company offers chatbot development solutions, with the office in Cochin, India. They are specialized in building web applications, chatbots and voice apps (Amazon Alexa and Google Assistants). They love challenges and thinks innovatively and disruptively to help our clients to turn their great ideas into reality. 3) PixelCrayons (Offer top chatbot development services in India) Location: India Pricing: $25-49 Employees: 400 Established: 2004 PixelCrayons is a well-known full-service best chatbot development company in India with its physical presence United Kingdom and the United States too. It offers a variety of web design/development and mobile full-service application development services including top chatbot development services in India. Its unmatched customized artificial intelligence and machine learning services to SMEs and startups are known all over the world and are beyond description. Its of the trusted chatbot companies help other  companies to deploy Messaging as a Platform (MAAP) model with tailored chatbot solutions. So, hire best chatbot developers from PixelCrayons and you will be able to save around 60% of development cost and get services of highly trained and skilled machine learning developers, quality infrastructure and all the modern technological developments. 4) E2logy (Best chatbot development company in India) Location: Ahmedabad, India Pricing: < $25 / hr Employees: 50 - 249 Established: 2005 E2logy is a full service top chatbot development company in India with expertise in any custom application development.Their focus area is Enterprise apps, Web applications & Websites, Mobile Apps, Remote Cloud Support and Digital Analytics Implementation. They make custom web applications based on client’s needs. They make mobile apps using Native SDKs of iOS, Android & Windows Phone.As one of the best chatbot companies, It offers services to transform the way businesses collaborate with customers and streamline interactions. 5) OnGraph Technologies Corporation (Best AI chatbot development service providers in India) Location: Noida, India Pricing: $25 - $49 / hr Employees: 50 - 249 Established: 2007 Founded in 2007, OnGraph is one of the best chatbot development firms in India. Since their establishment, they have brought custom software products to clients coming from small, mid-sized and large companies. Ongraph is in the list of top chatbot companies due to service offered by the them to transform the way business collaborate with clients and streamline interactions. Currently, they have rendered quality web and mobile development services to companies engaged in varied markets like health care, banking &amp; finance, eCommerce, education, telecom, media & entertainment, and more. 6) Affle Enterprise (One of the best chatbot development firms in India) Location: Gurgaon, India Pricing: $25 - $49 / hr Employees: 50 - 249 Established: 2005 Affle earned its position among the list of best chatbot companies in the industry due to proprietary consumer intelligence platform that delivers consumer engagement, acquisitions and transactions through relevant Mobile Advertising. The platform aims to enhance returns on marketing investment through contextual mobile ads and also by reducing digital ad fraud. 7) Enterpi Software Solutions (Top chatbot development company in India) Location: Hyderabad, India Pricing: < $25 / hrc Employees: 50 - 249 Established: 2008 EnterPi is a full spectrum software development company. Over the past 10 years, EnterPi has developed highly robust software systems for the worlds biggest companies. Applications built by us are used by multiple Fortune 500 companies with thousands of users, touching millions of customers. 8) IndiaNIC Infotech Ltd. (one of the best chatbot development firms in India) Pricing: < $25/hr Location: United States, India Employees: 250 - 999 Founded: 1998 Its main objective is to work with personalized web development, mobile application development, e-commerce business solutions, top chatbot development services with engineering practices guided by design and provide best chatbot development services in India. They have proficient engineers, artists, strategists, geeks and some rebellious personalities in their team, to make IndiaNIC the best place to be.They bring together intelligent and talented thinkers and help unleash their combined creativity in the world. 9) Chapter247 Infotech (One of the best chatbot development agencies in India) Location: Indore , India Pricing: < $25 / hr Employees: 10 - 49 Established: 2012 Chapter247 Infotech is a full service software partner specialized in architecting, designing & developing tech solutions by binding Web, IoT, Analytics, AI and Cloud technologies. Their global clients include organizations right from Enterprise, Midsize to startups and provide best chatbot development services in India. Chapter247 considered as the most reliable among other chatbot companies as they have talented chatbot developers who can make tedious job of creating bots for messaging platforms, website or a mobile a simple affair. In 6 years, they have launched over 100 products with ambitious clients in industries like Ecommerce, financial services, healthcare, education, shipping, energy and retail. 10) ELEKS (Best chatbot development company in India) Pricing: $25 - $49/hr Location: Ukraine, United States Employees: 1,000 - 9,999 Founded: 1991 They offer high-tech innovations to Fortune 500 companies, large companies, and technological challenges, improving the way they work and increasing the value they create for the modern world. They have more than 1,200 professionals located in delivery centers in Eastern Europe and sales offices in USA, Europe and Japan. They are helping their clients by digitally transforming their businesses through expert consulting, software engineering services and top chatbot development services.They provide their customers with a full range of software services. These include product development, dedicated equipment, R & D and technology consulting. 11) Ahex Technologies Pvt. Ltd. (Best among the list of chatbot compan ies in India) Location: India Pricing: < $25 / hr Employees: 50 - 249 Established: 2009 Founded in July 2009, Ahex Technologies is an ISO 9001:2015 certified software development Agency. They are provider of customized web application, mobile application, best chatbot development services in India and e-business solutions across the globe. 12) Quovantis Technologies (Best chatbot development company in India) Location: Noida, India Pricing: $25 - $49 / hr Employees: 50 - 249 Established: 2010 It is a full-lifecycle product development services company with a mission of creating beautiful products through design led product development. Quovantis Technologies has helped many leading product companies and innovative startups build delightful digital experiences. Some of the major clients include LOtus, Synaptics, LInklabs, Jason, Unity, Caarmo, Zoomvy etc. 13) TECHTIC SOLUTIONS INC (One of the top chatbot development agencies in India) Pricing: $25 - $49/hr Location: United States, India Employees: 50 - 249 Founded: 2009 Techtic Solutions Inc. is a leading company in the development of web and mobile applications known for offering innovative solutions especially top chatbot development services in India for any complex problem. It was incorporated in 2009 and based in the United States. It is an interactive creative agency, passionate about offering successful, efficient and successful solutions for your business. Whether it's a new company or a large corporation, they can offer innovative marketing strategies, exceptional brands, mobile application development and web solutions that exceed their expectations. 14) ArStudioz (One of the best chatbot development firms in India) Location: Jaipur, India Pricing: < $25 / hr Employees: 10 - 49 Established: 2017 ArStudioz is a solution-oriented team that develops user-friendly Mobile Apps and Software to help you increase engagement, transactions an overall satisfaction with their customers. They use the leading technology and systems to develop an app designed to your specifications that truly works and creates satisfaction among their customers. 15) ElkanIO Research Labs (One of the top chatbot development firms in India) Location: Cochin, India Pricing: < $25 / hr Employees: 2 - 9 Established: 2017 Founded in 2017 with a mindset to tackle the real world problems by using emerging digital technologies. A team with hands-on experience in developing Artificial Intelligence and Advanced Analytics solutions to cater to various industry needs. They have expertise in IoT-Software development support, Retail Analytics solutions, Predictive Maintenance Solutions and also provide top chatbot development services in India. 16) DevCom (Best chatbot development company in India) Pricing: $25 - $49/hr Location: Ukraine, India Employees: 50 - 249 Founded: 2000 DevCom proves that it was founded in the year 2000 and is a reliable provider of top chatbot development services in India for its customers worldwide. Devcom has carved a name for itself among the best chatbot companies by building AI chatbots that often outrun humans in virtual assistance. They understand that the value of technology lies in the benefits it provides to customers.This is the reason why they select among the best cutting-edge technologies to design solutions that meet customer needs. They comply with the latest technological standards and specialize in the development of software using all latest technologies. 17) XenonStack (Best chatbot development company in India) Location: Chandigarh, India Pricing: $25 - $49 / hr Employees: 50 - 249 Established: 2012 It is a Product Engineering and Technology Services company provides Digital enterprise services and solutions with DevOps , Big Data Engineering , Data Analytics , Cloud Migration ,Machine learning, Data Science and providing best chatbot development services in India. 18) Algoscale Technologies, Inc. (One of the best chatbot companies in India) Location: Noida, India Pricing: < $25 / hr Employees: 10 - 49 Established: 2014 Algoscale is a US incorporated, privately held boutique Big Data Analytics and Decision Science firm helping organizations derive valuable business insights from mountains of data through application of analytics and technology. It is helping organizations make strategic business decisions by deriving insights through application of analytical tools, techniques, and technology. 19) Webtunix AI (One of the top chatbot development companies in India) Location: Mohali, India Pricing: < $25 / hr Employees: 10 - 49 Established: 2015 Webtunix AI is a leading data science consulting company that is offering services in Chatbot development which are related to Industry needs. At Webtunix, they are offering services like data analytics services, machine learning services, data scraping, Data Mining, Object Detection, Video and Image Processing, Natural language processing and more. Hire best chatbot developers from this company. 20) XeliumTech Solutions (One of the best chatbot development services provider) Location: Gurgaon, India Pricing: < $25 / hr Employees: 50 - 249 Established: 2010 XeliumTech Solutions is a prominent Mobile App Development Company in Gurgaon, India, offering a range of Mobile App Design and Development, Android Application Development, IOS Application Development, Chatbot development, Cross-compatible, and Hybrid App Development Services, globally since 2010. They have a passionate team of skilled professionals who make it happen for them to deliver the niche solutions required by our valued customers. Conclusion: In this article, we have enlisted top 20 Chatbot development companies in India/USA. You can pick any company as per your business needs and budget constraint. Now, you will be definitely in a position to decide the best chatbot development firms in India after reading this article. I am ending by saying that picking the best chatbot development company in India seems easy, however, you must take all these points into account for your own good and successful business. Note: The list above is still in progress mode. If you like to include your Chatbot development company with us, then please write to us in the comment box.  Otherwise, you can message to us along with the details of the respective company.

Best Chatbot Development Companies For SME's and Startups in India, USA| Hire Best Chatbot Developer

Top 15 Outsourcing Software Development companies in India & USA for Startups & SME's | 2019

List of the Best Mobile App Development Companies & Developers — 2018 Reviews Is it a really harder for you to find best App Development company from a collection of thousands or maybe more?To save you from such a striving task, I collected here a prime list of the best app development companies in india. They have made business-growing apps since years and still making them with latest introduced technology. All of them are the solid ground of the best mobile app developers with exceptional technical skills plus heart winning strategies. So, what are you waiting for? Let’s start with the list of the best app development Companies in India: Best 10+ app development Companies in India 2018 IndiaNIC Infotech Ltd IndiaNIC is a leading mobile app, web and mobile games development company. Since 1998, we have successfully bagged 7000+ projects, building lifetime relationships with over 3500+ happy clients, for providing IT solutions to every industry sector out there. With the employee strength of over 400 mobile & web developers, designers and marketing experts working from our head office in Ahmedabad, Gujarat, we hail from a background of specialising in Mobile Apps, Games, Web Portal, Ecommerce, Custom Business Applications, Software Development using latest technologies as per the current and changing trends. Our expertise lies in Open Source Technologies, CMS’s, Microsoft Technologies, JAVA, MEAN Stake, iOS, Android, XAMARIN and other related trending tools and technologies. We are an ISO certified company and member of NASSCOM. Our client list includes names like Adidas, Cisco, Pepsi, Vodafone, McDonald’s, Gulfstream, Incentex, Jackson & Coker, Sancho BBDO, TATA, Kotak Mahindra Bank, Nirma Limited, and many more. Pricing: < $25/hr Location: India Employees: 250–999 Founded: 1998 2. Peerbits Peerbits is a global IT solution company delivering web development, mobile application development and IT consulting services of any complexity to clients worldwide. Peerbits has a strong team of 50+ highly skilled IT experts. Peerbits carries trust of more than 200+ clients spread across diverse business verticals.We are committed to excellence through the systematic and disciplined process. Our expertise guarantees quality, competence and rapid execution of any projects ranging from mid-sized portals to complex app development and custom mobility solutions.Our vision is to provide the utmost integrity and client satisfaction through our services, creating lasting relationships with our clients. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2011 3. Quytech An award winning bespoke mobile app development agency quy technology pvt ltd providing enterprise mobility solution, chat application, Augmented reality application & cross platform services . We have been rated among the top 20 most promising enterprise mobility company by CIO review. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2010 4. Finoit Finoit is a product engineering Company providing strategic and technology consulting in the field of Mobile, Software and IoT domain. We are focused on designing and developing customer-centric products and applications for startups to elite business groups across the globe. With our technology consulting and implementation services using Web, Mobile and IoT development technologies, we have delivered intuitive digital experiences and scalable products while solving business problems to over 250 customers across North America, Europe and Australia. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2010 5. Hopinfirst Hopinfirst is a leading edge custom software development, web and mobile offers development of websites, mobile applications, AR/AR development, Digital marketing services app development company Pricing: < $25/hr Location: India Employees: 10–49 Founded: 2016 6. konstantinfo Konstant Infosolutions is an industry leader in delivering successful Mobile and Web development solutions across the Globe. We have been working since 2003, and have 2500+ happy customers worldwide. Having delivered 3500+ successful applications, we have served 30+ large Enterprises as well as start-ups. We are committed to success and our endeavor is to build highly performant and cost-efficient solutions. We have partnered with distinguished industries and firms worldwide like Amazon Web Services, Member of iOS Developer Program, Registered Google Play Developer, Microsoft Certified Partner, Adobe Solution Partner, Intel Partner, Acquia Partner, Authorize.Net Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2003 7. Octalsoftware Octal IT Solution is a global IT service provider having a strong presence in UK, USA, Singapore and India. The company has the reputation of delivering niche solutions in the areas of web and mobile app development. Octal has repeatedly prescribed quality as its virtue. With the ISO 9001:2008 certification and strict discipline towards the prominent quality standards, the company has produced flawless results since last decade. In its history of ten years, Octal has earned notable associations from technology leaders like Microsoft, NASSCOM, Amazon Web Services, Acquia, STPI, Dun & Bradstreet and Drupal Organization etc. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2007 8. OpenXcell Incorporated in 2008 and headquartered in the USA, OpenXcell is an industry-leading software and mobile app development company known for delivering innovative software solutions and engaging mobile apps. Due to our unstoppable quest for making perfect mobile and web apps, we have slowly evolved into a one stop destination for all mobile and web app development needs. We have made a stellar reputation in the technology industry by adhering to strict timelines and quality deliverables. We are considered to be one of the most reliable partners by our clients as we follow a customer-oriented service model. Pricing: < $25/hr Location: India Employees: 50–249 Founded: 2009 9. 360 Degree Technosoft 360 Degree Technosoft is global solution provider of web development and mobile applications development dealing in various platforms like iOS and android. Having developed more than 500+ mobile applications on various platforms, 360 Degree Technosoft is well-accustomed with Google and Apple tools desired to develop a mobile application. Pricing: < $25/hr Location: India Employees: 10–49 Founded: 2010 10. Spaceotechnologies Space-O is one stop solution for all your mobile software development needs. From concept to development to marketing to ongoing maintenance, Space-O delivers. We are now one of the top mobile app development companies in India by following the success mantra of “Design-led-Engineering”. We work with best-of-the-best fine art grads from top design institutes such as NID and best-of-the-best engineers. Pricing: $25 — $49/hr Location: India Employees: 250–999 Founded: 2010 11. Cumulations Technologies Cumulations Technologies is Bangalore based most trusted mobile app development company serving to the industry from past 8 years. Delivered 100+ projects for more than 70 clients. Specialty lies in solving challenging problems and converting to a viable solution. More than 95% of Projects delivered on time. Worked in almost every industry and niche including smart home, healthcare, e-commerce, ticket booking, Wearable devices, Fintech and more. Pricing: $25 — $49/hr Location: India Employees: 10–49 Founded: 2010 12. Arka Softwares Arka Softwares is the one-stop solutions for all your mobile app requirements. From developing websites for industry giants to providing seamless user experience, there are plethoras of reasons that you can find to hire Arka Softwares as your development partner. With a team of 100+ developers and designers, Arka Softwares have delivered more than 100+ mobile apps to clients all across the globes. With a primary focus on agile development methodology, Arka Softwares offers great benefits to all the clients. As a custom mobile app development company, we provide robust, scalable and high-end development solutions to all our clients from small start-ups to large enterprise. Pricing: 18$ to 20$/hr Location: India, USA, UK Employee: 100+ Founded: 2010 These are the best app development companies in India that you can look out. You can also give me your suggestions in the comment box below. PS: This list is in progress and I update it every fortnight. Would you Like to list your App Development Company? Please do comment with your company name.

Best 10+ app development companies in India

THE OUTLOOK FOR THE GERMANS

Meet the Indian Startup That’s Building a Global Entertainment Platform on the Back of AI, and How

Süni intellektin arxasında Qlobal Əyləncə Platforması quran Hindistan Startapı ilə tanış olun və necə

Γνωρίστε την ινδική startup που χτίζει μια παγκόσμια πλατφόρμα ψυχαγωγίας στο πίσω μέρος του AI και πώς

Conozca la startup india que está construyendo una plataforma de entretenimiento global con inteligencia artificial y cómo

मिलिए उस भारतीय स्टार्टअप से जो AI के दम पर बना रहा है ग्लोबल एंटरटेनमेंट प्लेटफॉर्म, जानिए कैसे

Rankontre demaraj Endyen an ki ap konstwi yon platfòm Divètisman mondyal sou do AI, ak kijan

AIを活用したグローバルエンターテイメントプラットフォームを構築するインドのスタートアップ企業とその方法

AI артында жаһандық ойын-сауық платформасын құратын үнділік стартаппен танысыңыз және қалай

AI аркасында глобалдык көңүл ачуу платформасын куруп жаткан индиялык стартап менен таанышыңыз жана кантип

Sangana neIndia Kutanga Iko Kuvaka Global Varaidzo Platform kuseri kweAI, uye Sei

La kulan Bilowga Hindida Kaas oo Dhisaya Madal Madadaalada Caalamiga ah ee Dhabarka AI, iyo Sidee

Yapay Zekanın Arkasında Küresel Bir Eğlence Platformu Kuran Hintli Girişimle Tanışın ve Bunu Nasıl Yapıyor?

Gặp gỡ công ty khởi nghiệp Ấn Độ đang xây dựng nền tảng giải trí toàn cầu dựa trên AI và cách thức

First, the moral reason. Despite India being among the fastest growing economies of the world, and the world singing hosannas to the emerging Indian middle class, it scores extremely poorly on most human development indices. The fact that India now has a booming automobile industry with all manner of cars and SUVs choking the sidewalks of our urban centers cannot mask the co-existing abysmal poverty, that too is all too evident. Poverty is not an invisible underbelly in India. It is upfront and stark, but we all pretend that we don't see it. Much as the British colonialists went to their clubs, gymkhanas, and golf courses in pre-independence India, not caring to notice the plight of the common Indian who lived an emaciated existence, much of middle-class India, which is the backbone of the new corporate sector, is blind to the misery of their fellow Indians. This does not reflect well upon us as a people. Where is our moral conscience? How can we be so insensitive to our fellow human beings? The second and perhaps decisive reason is self-interest. The present economic development model is not tenable. It will lead to tremendous unrest. You have to make everybody join in the good times. Look at the sheer numbers of the deprived. How long will they silently watch as the privileged amongst us live better and bigger than ever before? Now more than ever before, we need everybody in the fold. The benefits that will accrue will be tremendous. As we help marginalized people join the mainstream, they will eventually become economic force multipliers. They will lead to economic activity of a scale that will be global in its ramifications. The government is doing what it can to help. The private sector has to pitch in big time- in our cities, towns, villages, and tribal areas. It has to build schools, plant trees, help with rainwater harvesting, clothe and feed the poor, and distribute books. There really is so much that can be done in India. It is heartening to see that companies in the corporate sector are now joining hands with the government and NGOs and helping with community development and environmental issues, besides pure philanthropy. This needs to continue and be taken to the next level. We need our Andrew Carnegies, Warren Buffets, Bill Gates, and Rockefellers. The smaller companies have to get their act together so that CSR is given the prime importance that is due to it. The media professionals have to pitch in by providing adequate coverage to all manner of CSR activity. PR and advertising plans should always have a CSR component, as the easiest thing to do in India is some form of social activity.  There is so much that is wrong and needs to be righted. From uncovered drains to dirty toilets, from streets without lighting to classrooms without books- there are so many causes that can be taken up. Let CSR be the number one priority for the next decade, and see how India moves up the human development indices. Only then will we be able to hold our own on the world stage. All the pretty people in the world driving around in their Ferraris won't do it for us. It is our masses who will do it for us. But they have to be clothed and fed first. First, the moral reason. Despite India being among the fastest growing economies of the world, and the world singing hosannas to the emerging Indian middle class, it scores extremely poorly on most human development indices. The fact that India now has a booming automobile industry with all manner of cars and SUVs choking the sidewalks of our urban centers cannot mask the co-existing abysmal poverty, that too is all too evident. Poverty is not an invisible underbelly in India. It is upfront and stark, but we all pretend that we don't see it. Much as the British colonialists went to their clubs, gymkhanas, and golf courses in pre-independence India, not caring to notice the plight of the common Indian who lived an emaciated existence, much of middle-class India, which is the backbone of the new corporate sector, is blind to the misery of their fellow Indians. This does not reflect well upon us as a people. gymkhanas, Where is our moral conscience? How can we be so insensitive to our fellow human beings? The second and perhaps decisive reason is self-interest. The present economic development model is not tenable. It will lead to tremendous unrest. You have to make everybody join in the good times. Look at the sheer numbers of the deprived. How long will they silently watch as the privileged amongst us live better and bigger than ever before? Now more than ever before, we need everybody in the fold. The benefits that will accrue will be tremendous. As we help marginalized people join the mainstream, they will eventually become economic force multipliers. They will lead to economic activity of a scale that will be global in its ramifications. The government is doing what it can to help. The private sector has to pitch in big time- in our cities, towns, villages, and tribal areas. It has to build schools, plant trees, help with rainwater harvesting, clothe and feed the poor, and distribute books. There really is so much that can be done in India. It is heartening to see that companies in the corporate sector are now joining hands with the government and NGOs and helping with community development and environmental issues, besides pure philanthropy. This needs to continue and be taken to the next level. We need our Andrew Carnegies, Warren Buffets, Bill Gates, and Rockefellers. The smaller companies have to get their act together so that CSR is given the prime importance that is due to it. The media professionals have to pitch in by providing adequate coverage to all manner of CSR activity. PR and advertising plans should always have a CSR component, as the easiest thing to do in India is some form of social activity.  There is so much that is wrong and needs to be righted. From uncovered drains to dirty toilets, from streets without lighting to classrooms without books- there are so many causes that can be taken up. Let CSR be the number one priority for the next decade, and see how India moves up the human development indices. Only then will we be able to hold our own on the world stage. All the pretty people in the world driving around in their Ferraris won't do it for us. It is our masses who will do it for us. But they have to be clothed and fed first.

CSR Is a Must for Indian Companies Irrespective of Size

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

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

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

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

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

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

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

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

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 참고자료

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

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

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

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

揭示印度推特上记者与政客互动中的性别偏见：参考文献

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

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

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

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

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

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

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

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

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 한계

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

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

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

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

揭示印度推特上记者与政客互动中的性别偏见：局限性

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

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

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

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