Anirban Sen
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Abstract
The structure and mechanics of organized outreach around certain issues, such as in propaganda networks, is constantly evolving on social media. We collect tweets on two propaganda events and one non-propaganda event with varying degrees of organized messaging. We then perform a comparative analysis of the user and network characteristics of social media networks around these events and find clearly distinguishable traits across events. We find that influential entities like prominent politicians, digital influencers, and mainstream media prefer to engage more with social media events with lesser degree of propaganda while avoiding events with high degree of propaganda, which are mostly sustained by lesser known but dedicated micro-influencers. We also find that network communities of events with high degree of propaganda are significantly centralized with respect to the influence exercised by their leaders. The methods and findings of this study can pave the way for modeling and early detection of other propaganda events, using their user and community characteristics.
- Norah Abokhodair, Daisy Yoo, and David W McDonald. 2015. Dissecting a social botnet: Growth, content and influence in Twitter. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing. 839--851.Google Scholar
Digital Library
- Nitin Agarwal, Samer Al-Khateeb, Rick Galeano, and Rebecca Goolsby. 2017. Examining the use of botnets and their evolution in propaganda dissemination. Defence Strategic Communications 2, 1 (2017), 87--112.Google ScholarCross Ref
- Syeda Zainab Akbar, Anmol Panda, Divyanshu Kukreti, Azhagu Meena, and Joyojeet Pal. 2021. Misinformation as a Window into Prejudice: COVID-19 and the Information Environment in India. Proceedings of the ACM on human-computer interaction 4, CSCW3 (2021), 1--28.Google ScholarDigital Library
- Syeda Zainab Akbar, Ankur Sharma, Himani Negi, Anmol Panda, and Joyojeet Pal. 2020. Anatomy of a Rumour: Social media and the suicide of Sushant Singh Rajput. arXiv preprint arXiv:2009.11744 (2020).Google Scholar
- Saja Aldera, Ahmad Emam, Muhammad Al-Qurishi, Majed Alrubaian, and Abdulrahman Alothaim. 2021. Online extremism detection in textual content: a systematic literature review. IEEE Access 9 (2021), 42384--42396.Google ScholarCross Ref
- Amit A Amleshwaram, Narasimha Reddy, Sandeep Yadav, Guofei Gu, and Chao Yang. 2013. Cats: Characterizing automation of twitter spammers. In 2013 Fifth International Conference on Communication Systems and Networks (COMSNETS). IEEE, 1--10.Google ScholarCross Ref
- Cynthia Andrews, Elodie Fichet, Yuwei Ding, Emma S Spiro, and Kate Starbird. 2016. Keeping up with the tweetdashians: The impact of'official'accounts on online rumoring. In Proceedings of the 19th ACM Conference on Computer- Supported Cooperative Work & Social Computing. 452--465.Google Scholar
- Arshia Arya, Soham De, Dibyendu Mishra, Gazal Shekhawat, Ankur Sharma, Anmol Panda, Faisal Lalani, Parantak Singh, Ramaravind Kommiya Mothilal, Rynaa Grover, et al. 2022. DISMISS: Database of Indian Social Media Influencers on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 16. 1201--1207.Google ScholarCross Ref
- Alberto Barrón-Cedeno, Giovanni Da San Martino, Israa Jaradat, and Preslav Nakov. 2019. Proppy: A system to unmask propaganda in online news. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 9847--9848.Google ScholarDigital Library
- Alberto Barrón-Cedeno, Israa Jaradat, Giovanni Da San Martino, and Preslav Nakov. 2019. Proppy: Organizing the news based on their propagandistic content. Information Processing & Management 56, 5 (2019), 1849--1864.Google ScholarDigital Library
- Jonah Berger and Katherine L Milkman. 2012. What makes online content viral? Journal of marketing research 49, 2 (2012), 192--205.Google ScholarCross Ref
- David M Beskow and Kathleen M Carley. 2018. Bot-hunter: a tiered approach to detecting & characterizing automated activity on twitter. In Conference paper. SBP-BRiMS: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation, Vol. 3. 3.Google Scholar
- Kiran Vinod Bhatia. 2022. Hindu nationalism online: Twitter as discourse and interface. Religions 13, 8 (2022), 739.Google ScholarCross Ref
- Shankar Biradar, Sunil Saumya, and Arun Chauhan. 2021. Hate or non-hate: Translation based hate speech identification in code-mixed hinglish data set. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2470--2475.Google ScholarCross Ref
- Gillian Bolsover and Philip Howard. 2019. Chinese computational propaganda: Automation, algorithms and the manipulation of information about Chinese politics on Twitter and Weibo. Information, communication & society 22, 14 (2019), 2063--2080.Google Scholar
- Alexandre Bovet and Hernán A Makse. 2019. Influence of fake news in Twitter during the 2016 US presidential election. Nature communications 10, 1 (2019), 1--14.Google Scholar
- Samantha Bradshaw, Hannah Bailey, and Philip N Howard. 2021. Industrialized disinformation: 2020 global inventory of organized social media manipulation. Computational Propaganda Project at the Oxford Internet Institute.Google Scholar
- Chiyu Cai, Linjing Li, and Daniel Zeng. 2017. Detecting social bots by jointly modeling deep behavior and content information. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1995--1998.Google ScholarDigital Library
- Guido Caldarelli, Rocco De Nicola, Fabio Del Vigna, Marinella Petrocchi, and Fabio Saracco. 2020. The role of bot squads in the political propaganda on Twitter. Communications Physics 3, 1 (2020), 1--15.Google ScholarCross Ref
- Hadley Cantril. 1938. Propaganda analysis. The English Journal 27, 3 (1938), 217--221.Google ScholarCross Ref
- Akemi Takeoka Chatfield, Christopher G Reddick, and Uuf Brajawidagda. 2015. Tweeting propaganda, radicalization and recruitment: Islamic state supporters multi-sided twitter networks. In Proceedings of the 16th annual international conference on digital government research. 239--249.Google ScholarDigital Library
- Nikan Chavoshi, Hossein Hamooni, and Abdullah Mueen. 2016. Debot: Twitter bot detection via warped correlation.. In Icdm. 817--822.Google Scholar
- TIMES FACT CHECK. 2019. FAKE ALERT: No UNESCO did not declare Indian national anthem ?best in the world'. https://timesofindia.indiatimes.com/times-fact-check/news/fake-alert-no-unesco-did-not-declare-indiannational- anthem-best-in-the-world/articleshow/70731926.cms?from=mdrGoogle Scholar
- Aditya Chetan, Brihi Joshi, Hridoy Sankar Dutta, and Tanmoy Chakraborty. 2019. Corerank: Ranking to detect users involved in blackmarket-based collusive retweeting activities. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. 330--338.Google ScholarDigital Library
- Rajdipa Chowdhury, Sriram Srinivasan, and Lise Getoor. 2020. Joint estimation of user and publisher credibility for fake news detection. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1993--1996.Google ScholarDigital Library
- Aaron Clauset, Mark EJ Newman, and Cristopher Moore. 2004. Finding community structure in very large networks. Physical review E 70, 6 (2004), 066111.Google Scholar
- Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeno, Rostislav Petrov, and Preslav Nakov. 2019. Finegrained analysis of propaganda in news article. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). 5636-- 5646.Google ScholarCross Ref
- Saloni Dash, Arshia Arya, Sukhnidh Kaur, and Joyojeet Pal. 2022. Narrative Building in Propaganda Networks on Indian Twitter. In Proceedings of the 14th ACM Web Science Conference 2022. 239--244.Google ScholarDigital Library
- Guillaume Daval-Frerot and Yannick Weis. 2020. WMD at SemEval-2020 Tasks 7 and 11: Assessing humor and propaganda using unsupervised data augmentation. In Proceedings of the Fourteenth Workshop on Semantic Evaluation. 1865--1874.Google ScholarCross Ref
- Sandipan Deb. August 2022. The politics of boycott Bollywood. https://www.moneycontrol.com/news/trends/ entertainment/the-politics-of-boycott-bollywood-9058811.html.Google Scholar
- Daryna Dementieva, Igor Markov, and Alexander Panchenko. 2020. SkoltechNLP at SemEval-2020 Task 11: Exploring unsupervised text augmentation for propaganda detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation. 1786--1792.Google ScholarCross Ref
- Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, and Giovanni Da San Martino. 2021. Detecting propaganda techniques in memes. arXiv preprint arXiv:2109.08013 (2021).Google Scholar
- Andrej Duh, Marjan Slak Rupnik, and Dean Koro?ak. 2018. Collective behavior of social bots is encoded in their temporal twitter activity. Big data 6, 2 (2018), 113--123.Google Scholar
- Juan Echeverr a, Emiliano De Cristofaro, Nicolas Kourtellis, Ilias Leontiadis, Gianluca Stringhini, and Shi Zhou. 2018. LOBO: Evaluation of generalization deficiencies in Twitter bot classifiers. In Proceedings of the 34th annual computer security applications conference. 137--146.Google ScholarDigital Library
- Murray Edelman. 1988. Constructing the political spectacle. University of Chicago Press.Google Scholar
- TimesNow Entertainment Desk. August 2022. Akshay Kumar lands in trouble; BJP MP Subramanian Swamy issues legal notice, accuses team of distorting Ram Setu saga. https://www.timesnownews.com/entertainment-news/akshaykumar-lands-in-trouble-bjp-mp-subramanian-swamy-issues-legal-notice-accuses-team-of-distorting-ram-setusaga- bollywood-news-entertainment-news-article-93830877.Google Scholar
- Vlad Ermurachi and Daniela Gifu. 2020. UAIC1860 at SemEval-2020 Task 11: Detection of propaganda techniques in news articles. In Proceedings of the Fourteenth Workshop on Semantic Evaluation. 1835--1840.Google ScholarCross Ref
- Dave Evans, Susan Bratton, and Jake McKee. 2021. Social media marketing. AG Printing & Publishing.Google Scholar
- Anna Fang and Zina Ben-Miled. 2017. Does bad news spread faster?. In 2017 International Conference on Computing, Networking and Communications (ICNC). IEEE, 793--797.Google ScholarCross Ref
- Gowhar Farooq. 2018. Politics of fake news: How WhatsApp became a potent propaganda tool in India. Media Watch 9, 1 (2018), 106--117.Google ScholarCross Ref
- Emilio Ferrara. 2017. Contagion dynamics of extremist propaganda in social networks. Information Sciences 418 (2017), 1--12.Google ScholarDigital Library
- Emilio Ferrara and Zeyao Yang. 2015. Measuring emotional contagion in social media. PloS one 10, 11 (2015), e0142390.Google ScholarCross Ref
- Berta García-Orosa. 2021. Disinformation, social media, bots, and astroturfing: the fourth wave of digital democracy. Profesional de la información 30, 6 (2021).Google Scholar
- Sonja Gensler, Franziska Völckner, Yuping Liu-Thompkins, and Caroline Wiertz. 2013. Managing brands in the social media environment. Journal of interactive marketing 27, 4 (2013), 242--256.Google ScholarCross Ref
- AM Goodwin, Katie Joseff, and Samuel C Woolley. 2020. Social media influencers and the 2020 US election: Paying ?regular people'for digital campaign communication. Center for Media Engagement (2020).Google Scholar
- Zaria Gorvett. 2020. A rumour about Covid-19 has been spreading -- that drinking water regularly and keeping your mouth moist can protect you. Here BBC Future examines the evidence. https://www.bbc.com/future/article/20200319- covid-19-will-drinking-water-keep-you-safe-from-coronavirusGoogle Scholar
- Dmitry Grigorev and Vladimir Ivanov. 2020. Inno at SemEval-2020 Task 11: Leveraging Pure Transformer for Multi-Class Propaganda Detection. arXiv preprint arXiv:2008.11584 (2020).Google Scholar
- Stefano Guarino, Noemi Trino, Alessandro Celestini, Alessandro Chessa, and Gianni Riotta. 2020. Characterizing networks of propaganda on twitter: a case study. Applied Network Science 5, 1 (2020), 1--22.Google ScholarCross Ref
- Mahdi Hashemi and Margeret Hall. 2019. Detecting and classifying online dark visual propaganda. Image and Vision Computing 89 (2019), 95--105.Google ScholarDigital Library
- Maryam Heidari, James H Jones, and Ozlem Uzuner. 2020. Deep contextualized word embedding for text-based online user profiling to detect social bots on twitter. In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 480--487.Google ScholarCross Ref
- Brian Heredia, Joseph Prusa, and Taghi Khoshgoftaar. 2017. Exploring the effectiveness of twitter at polling the united states 2016 presidential election. In 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC). IEEE, 283--290.Google ScholarCross Ref
- Garth S Jowett and Victoria O'donnell. 2018. Propaganda & persuasion. Sage publications.Google Scholar
- Franziska B Keller, David Schoch, Sebastian Stier, and JungHwan Yang. 2020. Political astroturfing on twitter: How to coordinate a disinformation campaign. Political communication 37, 2 (2020), 256--280.Google Scholar
- Sneha Kudugunta and Emilio Ferrara. 2018. Deep neural networks for bot detection. Information Sciences 467 (2018), 312--322.Google ScholarCross Ref
- Sangho Lee and Jong Kim. 2014. Early filtering of ephemeral malicious accounts on Twitter. Computer communications 54 (2014), 48--57.Google Scholar
- Yingya Li, Jieke Zhang, and Bei Yu. 2017. An NLP analysis of exaggerated claims in science news. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism. 106--111.Google ScholarCross Ref
- Ying Lin, Joe Hoover, Gwenyth Portillo-Wightman, Christina Park, Morteza Dehghani, and Heng Ji. 2018. Acquiring background knowledge to improve moral value prediction. In 2018 ieee/acm international conference on advances in social networks analysis and mining (asonam). IEEE, 552--559.Google Scholar
- Shenghua Liu, Bryan Hooi, and Christos Faloutsos. 2017. Holoscope: Topology-and-spike aware fraud detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1539--1548.Google ScholarDigital Library
- Yang Liu and Yi-Fang Wu. 2018. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.Google ScholarCross Ref
- Cristian Lumezanu, Nick Feamster, and Hans Klein. 2012. # bias: Measuring the tweeting behavior of propagandists. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 6. 210--217.Google Scholar
- Syed Mahbub, Eric Pardede, ASM Kayes, and Wenny Rahayu. 2019. Controlling astroturfing on the internet: a survey on detection techniques and research challenges. International journal of web and grid services 15, 2 (2019), 139--158.Google ScholarDigital Library
- Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, and Preslav Nakov. 2020. A survey on computational propaganda detection. arXiv preprint arXiv:2007.08024 (2020).Google Scholar
- Michele Mazza, Stefano Cresci, Marco Avvenuti,Walter Quattrociocchi, and Maurizio Tesconi. 2019. Rtbust: Exploiting temporal patterns for botnet detection on twitter. In Proceedings of the 10th ACM conference on web science. 183--192.Google ScholarDigital Library
- Liz McQuillan, Erin McAweeney, Alicia Bargar, and Alex Ruch. 2020. Cultural convergence: Insights into the behavior of misinformation networks on twitter. arXiv preprint arXiv:2007.03443 (2020).Google Scholar
- Bhaskar Mehta, Thomas Hofmann, and Peter Fankhauser. 2007. Lies and propaganda: detecting spam users in collaborative filtering. In Proceedings of the 12th international conference on Intelligent user interfaces. 14--21.Google ScholarDigital Library
- Nivedita Menon. 2020. Hindu Rashtra and Bollywood: A New Front in the Battle for Cultural Hegemony. South Asia Multidisciplinary Academic Journal 24/25 (2020).Google Scholar
- Abir Misra. 2021. Can the Celebrity Speak? Controversies and the Eulogistic Fandom of Shah Rukh Khan. American Behavioral Scientist (2021), 00027642211042286.Google Scholar
- B Vijay Murty. 2017. Jharkhand lynching: When a WhatsApp message turned tribals into killer mobs. Retrieved October 9, 2023 from https://www.hindustantimes.com/india-news/a-whatsapp-message-claimed-nine-lives-injharkhand- in-a-week/story-xZsIlwFawf82o5WTs8nhVL.htmlGoogle Scholar
- Yiftach Nagar. 2012. What do you think? The structuring of an online community as a collective-sensemaking process. In Proceedings of the ACM 2012 conference on computer supported cooperative work. 393--402.Google ScholarDigital Library
- Shobhana K. Nair. February 2022. Union Budget 2022 reactions | Budget has no vision for employment generation: Opposition. https://www.thehindu.com/business/budget/union-budget-2022-opposition-reaction/article38359128. ece.Google Scholar
- Leonardo Nizzoli, Marco Avvenuti, Stefano Cresci, and Maurizio Tesconi. 2019. Extremist propaganda tweet classification with deep learning in realistic scenarios. In Proceedings of the 10th ACM Conference on Web Science. 203--204.Google ScholarDigital Library
- Vitaliia-Anna Oliinyk, Victoria Vysotska, Yevhen Burov, Khrystyna Mykich, and Vítor Basto Fernandes. 2020. Propaganda Detection in Text Data Based on NLP and Machine Learning.. In MoMLeT DS. 132--144.Google Scholar
- Michael Orlov and Marina Litvak. 2019. Using behavior and text analysis to detect propagandists and misinformers on twitter. In Annual International Symposium on Information Management and Big Data. Springer, 67--74.Google ScholarCross Ref
- Diogo Pacheco, Alessandro Flammini, and Filippo Menczer. 2020. Unveiling coordinated groups behind white helmets disinformation. In Companion proceedings of the web conference 2020. 611--616.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Holly Paquette. 2013. Social media as a marketing tool: A literature review. (2013).Google Scholar
- Jiwoon Park, Ji Min Lee, Vikki Yiqi Xiong, Felix Septianto, and Yuri Seo. 2021. David and Goliath: when and why micro-influencers are more persuasive than mega-influencers. Journal of Advertising 50, 5 (2021), 584--602.Google ScholarCross Ref
- Jian Peng, Sam Detchon, Kim-Kwang Raymond Choo, and Helen Ashman. 2017. Astroturfing detection in social media: a binary n-gram--based approach. Concurrency and Computation: Practice and Experience 29, 17 (2017), e4013.Google ScholarCross Ref
- Charity Pradiptarini. 2011. Social media marketing: Measuring its effectiveness and identifying the target market. UW-L Journal of Undergraduate Research 14, 2 (2011), 2.Google Scholar
- Piotr Przybyla. 2020. Capturing the style of fake news. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 490--497.Google ScholarCross Ref
- Francisco Rangel and Paolo Rosso. 2019. Overview of the 7th author profiling task at PAN 2019: bots and gender profiling in twitter. In Proceedings of the CEUR Workshop, Lugano, Switzerland. 1--36.Google Scholar
- Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 conference on empirical methods in natural language processing. 2931--2937.Google ScholarCross Ref
- Nelson Ribeiro. 2014. Broadcasting to the Portuguese Empire in Africa: Salazar's singular broadcasting policy. Critical Arts 28, 6 (2014), 920--937.Google ScholarCross Ref
- Marcel Schliebs, Hannah Bailey, Jonathan Bright, and Philip N Howard. 2021. China's public diplomacy operations: understanding engagement and inauthentic amplifications of PRC diplomats on Facebook and Twitter. (2021).Google Scholar
- David Schoch, Franziska B Keller, Sebastian Stier, and JungHwan Yang. 2022. Coordination patterns reveal online political astroturfing across the world. Scientific reports 12, 1 (2022), 4572.Google Scholar
- Anirban Sen and Joyojeet Pal. September 2022. The Structure And Style Of A Dogma Community: Conspiracy Theories And Organized Twitter Engagement On Sushant Singh Rajput. https://www.medianama.com/2022/09/223- how-ssr-sushant-singh-rajput-fans-gamed-twitter-algorithm/.Google Scholar
- Hyunjin Seo. 2014. Visual propaganda in the age of social media: An empirical analysis of Twitter images during the 2012 Israeli--Hamas conflict. Visual Communication Quarterly 21, 3 (2014), 150--161.Google ScholarCross Ref
- Tathagat Sharma. 2020. The Pan India Experiment of NRC: History, Problems Associated and Lessons to Learn from Assam. (2020).Google Scholar
- Clay Shirky. 2009. Here comes everybody: How change happens when people come together. Penguin UK.Google Scholar
- Kai Shu, Deepak Mahudeswaran, Suhang Wang, and Huan Liu. 2020. Hierarchical propagation networks for fake news detection: Investigation and exploitation. In Proceedings of the international AAAI conference on web and social media, Vol. 14. 626--637.Google ScholarCross Ref
- Amila Silva, Yi Han, Ling Luo, Shanika Karunasekera, and Christopher Leckie. 2020. Embedding Partial Propagation Network for Fake News Early Detection.. In CIKM (Workshops), Vol. 2699.Google Scholar
- Kate Starbird. 2013. Delivering patients to sacré coeur: collective intelligence in digital volunteer communities. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 801--810.Google ScholarDigital Library
- Kate Starbird, Ahmer Arif, and Tom Wilson. 2019. Disinformation as collaborative work: Surfacing the participatory nature of strategic information operations. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1--26.Google ScholarDigital Library
- Briony Swire, Adam J Berinsky, Stephan Lewandowsky, and Ullrich KH Ecker. 2017. Processing political misinformation: Comprehending the Trump phenomenon. Royal Society open science 4, 3 (2017), 160802.Google Scholar
- Junfeng Tian, Min Gui, Chenliang Li, Ming Yan, and Wenming Xiao. 2021. MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). 1082--1087.Google ScholarCross Ref
- Javier Torregrosa, Joshua Thorburn, Raúl Lara-Cabrera, David Camacho, and Humberto M Trujillo. 2020. Linguistic analysis of pro-isis users on twitter. Behavioral Sciences of Terrorism and Political Aggression 12, 3 (2020), 171--185.Google ScholarCross Ref
- Andrea Tundis, Gaurav Mukherjee, and Max Mühlhäuser. 2020. Mixed-code text analysis for the detection of online hidden propaganda. In Proceedings of the 15th International Conference on Availability, Reliability and Security. 1--7.Google ScholarDigital Library
- Shylaja Varma. February 2020. ?They Are Calling Me Terrorist": Defiant, BJP's Kapil Mishra TweetsAgain. https://www. ndtv.com/delhi-news/delhi-violence-bjps-kapil-mishra-tweets-again-says-they-are-calling-me-terrorist-2185804.Google Scholar
- Sarah Vieweg, Amanda L Hughes, Kate Starbird, and Leysia Palen. 2010. Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In Proceedings of the SIGCHI conference on human factors in computing systems. 1079--1088.Google ScholarDigital Library
- Simona Vinerean, Iuliana Cetina, Luigi Dumitrescu, and Mihai Tichindelean. 2013. The effects of social media marketing on online consumer behavior. International journal of business and management 8, 14 (2013), 66.Google ScholarCross Ref
- Nishan Chathuranga Wickramarathna, Thiruni D Jayasiriwardena, Malith Wijesekara, Pasindu Bawantha Munasinghe, and Gamage Upeksha Ganegoda. 2020. A framework to detect twitter platform manipulation and computational propaganda. In 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 214--219.Google ScholarCross Ref
- Alex Williams Kirkpatrick. 2021. The spread of fake science: Lexical concreteness, proximity, misinformation sharing, and the moderating role of subjective knowledge. Public Understanding of Science 30, 1 (2021), 55--74.Google ScholarCross Ref
- William Williamson III and James Scrofani. 2019. Trends in detection and characterization of propaganda bots. (2019).Google Scholar
- Kai-Cheng Yang, Francesco Pierri, Pik-Mai Hui, David Axelrod, Christopher Torres-Lugo, John Bryden, and Filippo Menczer. 2021. The covid-19 infodemic: Twitter versus facebook. Big Data & Society 8, 1 (2021), 20539517211013861.Google ScholarCross Ref
- Kai-Cheng Yang, Onur Varol, Clayton A Davis, Emilio Ferrara, Alessandro Flammini, and Filippo Menczer. 2019. Arming the public with artificial intelligence to counter social bots. Human Behavior and Emerging Technologies 1, 1 (2019), 48--61.Google ScholarCross Ref
- Zichao Yang, Zhiting Hu, Chris Dyer, Eric P Xing, and Taylor Berg-Kirkpatrick. 2018. Unsupervised text style transfer using language models as discriminators. Advances in Neural Information Processing Systems 31 (2018).Google Scholar
- Zhi Yang, Jilong Xue, Xiaoyong Yang, Xiao Wang, and Yafei Dai. 2015. VoteTrust: Leveraging friend invitation graph to defend against social network sybils. IEEE Transactions on dependable and secure computing 13, 4 (2015), 488--501.Google ScholarDigital Library
Index Terms
- Networks and Influencers in Online Propaganda Events: A Comparative Study of Three Cases in India
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