* [206] Shervin Malmasi and Marcos Zampieri.  Challenges in discriminating profanity from hate speech.  Journal of Experimental & Theoretical Artificial Intelligence, 30(2):187–202, 2018.
  * [207] Daniel Martens and Walid Maalej.  Towards understanding and detecting fake reviews in app stores.  Empirical Software Engineering, 24(6):3316–3355, 2019.
  * [208] Binny Mathew, Punyajoy Saha, Hardik Tharad, Subham Rajgaria, Prajwal Singhania, Suman Kalyan Maity, Pawan Goyal, and Animesh Mukherjee.  Thou shalt not hate: Countering online hate speech.  In Proceedings of the international AAAI conference on web and social media, volume 13, pages 369–380, 2019.
  * [209] J Nathan Matias.  The civic labor of online moderators.  In Internet Politics and Policy conference. Oxford, United Kingdom, 2016.
  * [210] Patrick McDaniel, Nicolas Papernot, and Z Berkay Celik.  Machine learning in adversarial settings.  IEEE Security & Privacy, 14(3):68–72, 2016.
  * [211] Nikhil Mehta, María Leonor Pacheco, and Dan Goldwasser.  Tackling fake news detection by continually improving social context representations using graph neural networks.  In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1363–1380, 2022.
  * [212] Shahan Ali Memon and Kathleen M Carley.  Characterizing covid-19 misinformation communities using a novel twitter dataset.  arXiv preprint arXiv:2008.00791, 2020.
  * [213] Paul Mena.  Cleaning up social media: The effect of warning labels on likelihood of sharing false news on facebook.  Policy & internet, 12(2):165–183, 2020.
  * [214] Ministry of Electronics and Information Technology.  Information technology(intermediary guidelines and digital media ethics code) rules, 2021.  https://mib.gov.in/sites/default/files/IT%28Intermediary%20Guidelines%20and%20Digital%20Media%20Ethics%20Code%29%20Rules%2C%202021%20English.pdf, 2021\.
  * [215] Ministry of Electronics and Information Technology.  Information technology(intermediary guidelines and digital media ethics code) rules, 2021.  https://www.meity.gov.in/writereaddata/files/Intermediary_Guidelines_and_Digital_Media_Ethics_Code_Rules-2021.pdf, 2021\.
  * [216] Rahul Mishra.  Fake news detection using higher-order user to user mutual-attention progression in propagation paths.  In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 652–653, 2020.
  * [217] Sandip Modha, Prasenjit Majumder, and Daksh Patel.  Da-ld-hildesheim at semeval-2019 task 6: tracking offensive content with deep learning using shallow representation.  In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 577–581, 2019.
  * [218] Ayme Arango Monnar, Jorge Pérez, Barbara Poblete, Magdalena Saldaña, and Valentina Proust.  Resources for multilingual hate speech detection.  In Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), pages 122–130, 2022.
  * [219] Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M Bronstein.  Fake news detection on social media using geometric deep learning.  2019\.
  * [220] Marzieh Mozafari, Reza Farahbakhsh, and Noel Crespi.  A bert-based transfer learning approach for hate speech detection in online social media.  In International Conference on Complex Networks and Their Applications, pages 928–940. Springer, 2019.
  * [221] Marzieh Mozafari, Reza Farahbakhsh, and Noël Crespi.  Hate speech detection and racial bias mitigation in social media based on bert model.  PloS one, 15(8):e0237861, 2020.
  * [222] Arjun Mukherjee, Bing Liu, and Natalie Glance.  Spotting fake reviewer groups in consumer reviews.  In Proceedings of the 21st international conference on World Wide Web, pages 191–200, 2012.
  * [223] Kevin Munger.  Tweetment effects on the tweeted: Experimentally reducing racist harassment.  Political Behavior, 39(3):629–649, 2017.
  * [224] Tyler Musgrave, Alia Cummings, and Sarita Schoenebeck.  Experiences of harm, healing, and joy among black women and femmes on social media.  In CHI Conference on Human Factors in Computing Systems, pages 1–17, 2022.
  * [225] Sarah Myers West.  Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms.  New Media & Society, 20(11):4366–4383, 2018.
  * [226] Xia-mu Niu and Yu-hua Jiao.  An overview of perceptual hashing.  ACTA ELECTONICA SINICA, 36(7):1405, 2008.
  * [227] Debora Nozza.  Exposing the limits of zero-shot cross-lingual hate speech detection.  In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 907–914, 2021.
  * [228] Brendan Nyhan, Ethan Porter, Jason Reifler, and Thomas J Wood.  Taking fact-checks literally but not seriously? the effects of journalistic fact-checking on factual beliefs and candidate favorability.  Political Behavior, 42(3):939–960, 2020.
  * [229] Nedjma Ousidhoum, Zizheng Lin, Hongming Zhang, Yangqiu Song, and Dit-Yan Yeung.  Multilingual and multi-aspect hate speech analysis.  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), pages 4675–4684, Hong Kong, China, November 2019. Association for Computational Linguistics.
  * [230] Christina A. Pan, Sahil Yakhmi, Tara P. Iyer, Evan Strasnick, Amy X. Zhang, and Michael S. Bernstein.  Comparing the perceived legitimacy of content moderation processes: Contractors, algorithms, expert panels, and digital juries.  Proc. ACM Hum.-Comput. Interact., 6(CSCW1), apr 2022.
  * [231] Jeff Z Pan, Siyana Pavlova, Chenxi Li, Ningxi Li, Yangmei Li, and Jinshuo Liu.  Content based fake news detection using knowledge graphs.  In International semantic web conference, pages 669–683. Springer, 2018.
  * [232] Orestis Papakyriakopoulos and Ellen Goodman.  The impact of twitter labels on misinformation spread and user engagement: Lessons from trump’s election tweets.  In Proceedings of the ACM Web Conference 2022, pages 2541–2551, 2022.
  * [233] Ji Ho Park and Pascale Fung.  One-step and two-step classification for abusive language detection on Twitter.  In Proceedings of the First Workshop on Abusive Language Online, pages 41–45, Vancouver, BC, Canada, August 2017. Association for Computational Linguistics.
  * [234] Sungkyu Park, Jamie Yejean Park, Hyojin Chin, Jeong-han Kang, and Meeyoung Cha.  An experimental study to understand user experience and perception bias occurred by fact-checking messages.  In Proceedings of the Web Conference 2021, pages 2769–2780, 2021\.
  * [235] Demetris Paschalides, Dimosthenis Stephanidis, Andreas Andreou, Kalia Orphanou, George Pallis, Marios D Dikaiakos, and Evangelos Markatos.  Mandola: A big-data processing and visualization platform for monitoring and detecting online hate speech.  ACM Transactions on Internet Technology (TOIT), 20(2):1–21, 2020\.
  * [236] Gordon Pennycook, Adam Bear, Evan T Collins, and David G Rand.  The implied truth effect: Attaching warnings to a subset of fake news headlines increases perceived accuracy of headlines without warnings.  Management Science, 66(11):4944–4957, 2020.
  * [237] Georgios K Pitsilis, Heri Ramampiaro, and Helge Langseth.  Detecting offensive language in tweets using deep learning.  arXiv preprint arXiv:1801.04433, 2018.
  * [238] Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein.  A stylometric inquiry into hyperpartisan and fake news.  In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 231–240, 2018.
  * [239] Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty.  MOMENTA: A multimodal framework for detecting harmful memes and their targets.  In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4439–4455, Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics.
  * [240] Peng Qi, Juan Cao, Tianyun Yang, Junbo Guo, and Jintao Li.  Exploiting multi-domain visual information for fake news detection.  In 2019 IEEE international conference on data mining (ICDM), pages 518–527. IEEE, 2019.
  * [241] Feng Qian, Chengyue Gong, Karishma Sharma, and Yan Liu.  Neural user response generator: Fake news detection with collective user intelligence.  In IJCAI, volume 18, pages 3834–3840, 2018.
  * [242] Jing Qian, Mai ElSherief, Elizabeth Belding, and William Yang Wang.  Hierarchical CVAE for fine-grained hate speech classification.  In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3550–3559, Brussels, Belgium, October-November 2018. Association for Computational Linguistics.
  * [243] Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi.  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, pages 2931–2937, 2017.
  * [244] Adrian Rauchfleisch and Jonas Kaiser.  Deplatforming the far-right: An analysis of youtube and bitchute.  Available at SSRN, 2021.
  * [245] Sahil Raut, Nikhil Mhatre, Sanskar Jha, and Aditi Chhabria.  Hate classifier for social media platform using tree lstm.  In ITM Web of Conferences, volume 44, page 03034. EDP Sciences, 2022\.
  * [246] Yuxiang Ren, Bo Wang, Jiawei Zhang, and Yi Chang.  Adversarial active learning based heterogeneous graph neural network for fake news detection.  In 2020 IEEE International Conference on Data Mining (ICDM), pages 452–461. IEEE, 2020.
  * [247] Alison Ribeiro and Nádia Silva.  Inf-hateval at semeval-2019 task 5: Convolutional neural networks for hate speech detection against women and immigrants on twitter.  In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 420–425, 2019.
  * [248] Martin J Riedl, Kelsey N Whipple, and Ryan Wallace.  Antecedents of support for social media content moderation and platform regulation: the role of presumed effects on self and others.  Information, Communication & Society, pages 1–18, 2021.
  * [249] Sarah T Roberts.  Commercial content moderation: Digital laborers’ dirty work.  2016\.
  * [250] Sarah T Roberts.  Digital detritus:’error’and the logic of opacity in social media content moderation.  First Monday, 2018.
  * [251] Richard Rogers.  Deplatforming: Following extreme internet celebrities to telegram and alternative social media.  European Journal of Communication, 35(3):213–229, 2020.
  * [252] Björn Ross, Anna Jung, Jennifer Heisel, and Stefan Stieglitz.  Fake news on social media: The (in) effectiveness of warning messages.  2018\.
  * [253] Alon Rozental and Dadi Biton.  Amobee at SemEval-2019 tasks 5 and 6: Multiple choice CNN over contextual embedding.  In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 377–381, Minneapolis, Minnesota, USA, June 2019. Association for Computational Linguistics.
  * [254] Natali Ruchansky, Sungyong Seo, and Yan Liu.  Csi: A hybrid deep model for fake news detection.  In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 797–806, 2017.
  * [255] Punyajoy Saha, Binny Mathew, Pawan Goyal, and Animesh Mukherjee.  Hateminers: detecting hate speech against women.  arXiv preprint arXiv:1812.06700, 2018.
  * [256] Punyajoy Saha, Binny Mathew, Pawan Goyal, and Animesh Mukherjee.  Hatemonitors: Language agnostic abuse detection in social media.  arXiv preprint arXiv:1909.12642, 2019.
  * [257] Haji Mohammad Saleem and Derek Ruths.  The aftermath of disbanding an online hateful community.  arXiv preprint arXiv:1804.07354, 2018.
  * [258] Joni Salminen, Hind Almerekhi, Milica Milenković, Soon-gyo Jung, Jisun An, Haewoon Kwak, and Bernard J Jansen.  Anatomy of online hate: developing a taxonomy and machine learning models for identifying and classifying hate in online news media.  In Twelfth International AAAI Conference on Web and Social Media, 2018.
  * [259] Joni Salminen, Maximilian Hopf, Shammur A Chowdhury, Soon-gyo Jung, Hind Almerekhi, and Bernard J Jansen.  Developing an online hate classifier for multiple social media platforms.  Human-centric Computing and Information Sciences, 10(1):1–34, 2020\.
  * [260] Emily Saltz, Claire R Leibowicz, and Claire Wardle.  Encounters with visual misinformation and labels across platforms: An interview and diary study to inform ecosystem approaches to misinformation interventions.  In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI EA ’21, New York, NY, USA, 2021. Association for Computing Machinery.
  * [261] Nithya Sambasivan, Amna Batool, Nova Ahmed, Tara Matthews, Kurt Thomas, Laura Sanely Gaytán-Lugo, David Nemer, Elie Bursztein, Elizabeth Churchill, and Sunny Consolvo.  ” they don’t leave us alone anywhere we go” gender and digital abuse in south asia.  In proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–14, 2019.
  * [262] Justin Sampson, Fred Morstatter, Liang Wu, and Huan Liu.  Leveraging the implicit structure within social media for emergent rumor detection.  In Proceedings of the 25th ACM international on conference on information and knowledge management, pages 2377–2382, 2016.
  * [263] Manuela Sanguinetti, Fabio Poletto, Cristina Bosco, Viviana Patti, and Marco Stranisci.  An italian twitter corpus of hate speech against immigrants.  In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018), 2018.
  * [264] Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith.  The risk of racial bias in hate speech detection.  In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1668–1678, Florence, Italy, July 2019. Association for Computational Linguistics.
  * [265] Anna Schmidt and Michael Wiegand.  A survey on hate speech detection using natural language processing.  In Proceedings of the fifth international workshop on natural language processing for social media, pages 1–10, 2017.
  * [266] Sarita Schoenebeck, Oliver L Haimson, and Lisa Nakamura.  Drawing from justice theories to support targets of online harassment.  new media & society, 23(5):1278–1300, 2021.
  * [267] Sarita Schoenebeck, Carol F Scott, Emma Grace Hurley, Tammy Chang, and Ellen Selkie.  Youth trust in social media companies and expectations of justice: Accountability and repair after online harassment.  Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1):1–18, 2021.
  * [268] Christof Schuster.  A note on the interpretation of weighted kappa and its relations to other rater agreement statistics for metric scales.  Educational and Psychological Measurement, 64(2):243–253, 2004\.
  * [269] Joseph Seering.  Reconsidering community self-moderation: the role of research in supporting community-based models for online content moderation.  Proc. ACM Hum.-Comput. Interact, 3, 2020.
  * [270] Joseph Seering, Tony Wang, Jina Yoon, and Geoff Kaufman.  Moderator engagement and community development in the age of algorithms.  New Media & Society, 21(7):1417–1443, 2019.
  * [271] Haeseung Seo, Aiping Xiong, and Dongwon Lee.  Trust it or not: Effects of machine-learning warnings in helping individuals mitigate misinformation.  In Proceedings of the 10th ACM Conference on Web Science, pages 265–274, 2019.
  * [272] Haeseung Seo, Aiping Xiong, Sian Lee, and Dongwon Lee.  If you have a reliable source, say something: Effects of correction comments on covid-19 misinformation.  In Proceedings of the International AAAI Conference on Web and Social Media, volume 16, pages 896–907, 2022.
  * [273] Gautam Kishore Shahi, Anne Dirkson, and Tim A Majchrzak.  An exploratory study of covid-19 misinformation on twitter.  Online Social Networks and Media, 22:100104, 2021.
  * [274] Lanyu Shang, Ziyi Kou, Yang Zhang, and Dong Wang.  A multimodal misinformation detector for covid-19 short videos on tiktok.  In 2021 IEEE International Conference on Big Data (Big Data), pages 899–908. IEEE, 2021.
  * [275] Filipo Sharevski, Raniem Alsaadi, Peter Jachim, and Emma Pieroni.  Misinformation warnings: Twitter’s soft moderation effects on covid-19 vaccine belief echoes.  Computers & security, 114:102577, 2022.
  * [276] Karishma Sharma, Yizhou Zhang, Emilio Ferrara, and Yan Liu.  Identifying coordinated accounts on social media through hidden influence and group behaviours.  KDD ’21, New York, NY, USA, 2021. Association for Computing Machinery.
  * [277] Qinlan Shen and Carolyn Rose.  The discourse of online content moderation: Investigating polarized user responses to changes in reddit’s quarantine policy.  In Proceedings of the Third Workshop on Abusive Language Online, pages 58–69, 2019.
  * [278] Qinlan Shen and Carolyn P Rosé.  A tale of two subreddits: Measuring the impacts of quarantines on political engagement on reddit.  In Proceedings of the International AAAI Conference on Web and Social Media, volume 16, pages 932–943, 2022.
  * [279] Imani N Sherman, Jack W Stokes, and Elissa M Redmiles.  Designing media provenance indicators to combat fake media.  In 24th International Symposium on Research in Attacks, Intrusions and Defenses, pages 324–339, 2021.
  * [280] Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu.  defend: Explainable fake news detection.  In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 395–405, 2019.
  * [281] Kai Shu, Deepak Mahudeswaran, Suhang Wang, and Huan Liu.  Hierarchical propagation networks for fake news detection: Investigation and exploitation.  In Proceedings of the international AAAI conference on web and social media, volume 14, pages 626–637, 2020.
  * [282] Kai Shu, Suhang Wang, and Huan Liu.  Understanding user profiles on social media for fake news detection.  In 2018 IEEE conference on multimedia information processing and retrieval (MIPR), pages 430–435. IEEE, 2018.
  * [283] Kai Shu, Suhang Wang, and Huan Liu.  Beyond news contents: The role of social context for fake news detection.  In Proceedings of the twelfth ACM international conference on web search and data mining, pages 312–320, 2019.
  * [284] Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, and Huan Liu.  The role of user profiles for fake news detection.  In Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, pages 436–439, 2019.
  * [285] Vivek K Singh, Souvick Ghosh, and Christin Jose.  Toward multimodal cyberbullying detection.  In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pages 2090–2099, 2017.
  * [286] Mohit Singhal.  Analysis and Categorization of Drive-By Download Malware Using Sandboxing and Yara Ruleset.  PhD thesis, The University of Texas at Arlington, 2019.
  * [287] Mohit Singhal, Nihal Kumarswamy, Shreyasi Kinhekar, and Shirin Nilizadeh.  The prevalence of cybersecurity misinformation on social media: Case studies on phishing reports and zoom’s threats.  arXiv preprint arXiv:2110.12296, 2021.
  * [288] Shivangi Singhal, Tanisha Pandey, Saksham Mrig, Rajiv Ratn Shah, and Ponnurangam Kumaraguru.  Leveraging intra and inter modality relationship for multimodal fake news detection.  In Companion Proceedings of the Web Conference 2022, pages 726–734, 2022.
  * [289] Michael Soha and Zachary J McDowell.  Monetizing a meme: Youtube, content id, and the harlem shake.  Social Media+ Society, 2(1):2056305115623801, 2016.
  * [290] Chenguang Song, Nianwen Ning, Yunlei Zhang, and Bin Wu.  A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks.  Information Processing & Management, 58(1):102437, 2021.
  * [291] Chenguang Song, Kai Shu, and Bin Wu.  Temporally evolving graph neural network for fake news detection.  Information Processing & Management, 58(6):102712, 2021.
  * [292] Saurabh Srivastava, Prerna Khurana, and Vartika Tewari.  Identifying aggression and toxicity in comments using capsule network.  In Proceedings of the first workshop on trolling, aggression and cyberbullying (TRAC-2018), pages 98–105, 2018.
  * [293] Nili Steinfeld.  “i agree to the terms and conditions”:(how) do users read privacy policies online? an eye-tracking experiment.  Computers in human behavior, 55:992–1000, 2016.
  * [294] John Suler.  The online disinhibition effect.  Cyberpsychology & behavior, 7(3):321–326, 2004.
  * [295] Nicolas Suzor, Tess Van Geelen, and Sarah Myers West.  Evaluating the legitimacy of platform governance: A review of research and a shared research agenda.  International Communication Gazette, 80(4):385–400, 2018.
  * [296] Nicolas P Suzor, Sarah Myers West, Andrew Quodling, and Jillian York.  What do we mean when we talk about transparency? toward meaningful transparency in commercial content moderation.  International Journal of Communication, 13:18, 2019.
  * [297] Eric S Tellez, Daniela Moctezuma, Sabino Miranda-Jiménez, and Mario Graff.  An automated text categorization framework based on hyperparameter optimization.  Knowledge-Based Systems, 149:110–123, 2018.
  * [298] Kurt Thomas, Patrick Gage Kelley, Sunny Consolvo, Patrawat Samermit, and Elie Bursztein.  “it’s common and a part of being a content creator”: Understanding how creators experience and cope with hate and harassment online.  In CHI Conference on Human Factors in Computing Systems, pages 1–15, 2022.
  * [299] Pamela Bilo Thomas, Daniel Riehm, Maria Glenski, and Tim Weninger.  Behavior change in response to subreddit bans and external events.  IEEE Transactions on Computational Social Systems, 8(4):809–818, 2021.
  * [300] Amaury Trujillo and Stefano Cresci.  Make reddit great again: Assessing community effects of moderation interventions on r/the_donald.  Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 2022\.
  * [301] Sebastian Tschiatschek, Adish Singla, Manuel Gomez Rodriguez, Arpit Merchant, and Andreas Krause.  Fake news detection in social networks via crowd signals.  In Companion proceedings of the the web conference 2018, pages 517–524, 2018.
  * [302] Jacqueline Urakami, Yeongdae Kim, Hiroki Oura, and Katie Seaborn.  Finding strategies against misinformation in social media: A qualitative study.  In CHI Conference on Human Factors in Computing Systems Extended Abstracts, pages 1–7, 2022.
  * [303] US Copyright Office, Washington, DC, USA.  Copyright Law of the United States (Title 17), 2021 [Online].
  * [304] Kristen Vaccaro, Christian Sandvig, and Karrie Karahalios.  ” at the end of the day facebook does what itwants” how users experience contesting algorithmic content moderation.  Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2):1–22, 2020.
  * [305] Ameya Vaidya, Feng Mai, and Yue Ning.  Empirical analysis of multi-task learning for reducing identity bias in toxic comment detection.  In Proceedings of the International AAAI Conference on Web and Social Media, volume 14, pages 683–693, 2020.
  * [306] Rama Adithya Varanasi, Joyojeet Pal, and Aditya Vashistha.  Accost, accede, or amplify: Attitudes towards covid-19 misinformation on whatsapp in india.  In CHI Conference on Human Factors in Computing Systems, pages 1–17, 2022.
  * [307] Francielle Alves Vargas, Fabiana Rodrigues de Góes, Isabelle Carvalho, Fabrício Benevenuto, and Thiago Alexandre Salgueiro Pardo.  Contextual lexicon-based approach for hate speech and offensive language detection.  arXiv preprint arXiv:2104.12265, 2021.
  * [308] Neeraj Vashistha and Arkaitz Zubiaga.  Online multilingual hate speech detection: experimenting with hindi and english social media.  Information, 12(1):5, 2020.
  * [309] Riza Velioglu and Jewgeni Rose.  Detecting hate speech in memes using multimodal deep learning approaches: Prize-winning solution to hateful memes challenge.  arXiv preprint arXiv:2012.12975, 2020.
  * [310] Bertie Vidgen and Taha Yasseri.  Detecting weak and strong islamophobic hate speech on social media.  Journal of Information Technology & Politics, 17(1):66–78, 2020\.
  * [311] Prashanth Vijayaraghavan, Hugo Larochelle, and Deb Roy.  Interpretable multi-modal hate speech detection.  arXiv preprint arXiv:2103.01616, 2021.
  * [312] Nguyen Vo and Kyumin Lee.  The rise of guardians: Fact-checking url recommendation to combat fake news.  In The 41st international ACM SIGIR conference on research & development in information retrieval, pages 275–284, 2018.
  * [313] Svitlana Volkova and Jin Yea Jang.  Misleading or falsification: Inferring deceptive strategies and types in online news and social media.  In Companion Proceedings of the The Web Conference 2018, pages 575–583, 2018.
  * [314] William Yang Wang.  “liar, liar pants on fire”: A new benchmark dataset for fake news detection.  In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 422–426, Vancouver, Canada, July 2017. Association for Computational Linguistics.
  * [315] Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao.  Eann: Event adversarial neural networks for multi-modal fake news detection.  In Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining, pages 849–857, 2018.
  * [316] Richard Ashby Wilson and Molly K Land.  Hate speech on social media: Content moderation in context.  Conn. L. Rev., 52:1029, 2020.
  * [317] Kevin Winter and Roman Kern.  Know-center at semeval-2019 task 5: multilingual hate speech detection on twitter using cnns.  In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 431–435, 2019.
  * [318] Liang Wu and Huan Liu.  Tracing fake-news footprints: Characterizing social media messages by how they propagate.  In Proceedings of the eleventh ACM international conference on Web Search and Data Mining, pages 637–645, 2018.
  * [319] Liang Wu, Fred Morstatter, Xia Hu, and Huan Liu.  Mining misinformation in social media.  Big data in complex and social networks, pages 123–152, 2016.
  * [320] Yang Wu, Pengwei Zhan, Yunjian Zhang, Liming Wang, and Zhen Xu.  Multimodal fusion with co-attention networks for fake news detection.  In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2560–2569, 2021.
  * [321] Ellery Wulczyn, Nithum Thain, and Lucas Dixon.  Ex machina: Personal attacks seen at scale.  In Proceedings of the 26th international conference on world wide web, pages 1391–1399, 2017.
  * [322] Tomer Wullach, Amir Adler, and Einat Minkov.  Fight fire with fire: Fine-tuning hate detectors using large samples of generated hate speech.  In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4699–4705, Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics.
  * [323] Sijia Xiao, Coye Cheshire, and Niloufar Salehi.  Sensemaking, support, safety, retribution, transformation: A restorative justice approach to understanding adolescents’ needs for addressing online harm.  In CHI Conference on Human Factors in Computing Systems, pages 1–15, 2022.
  * [324] Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, and Liang Wang.  Evidence-aware fake news detection with graph neural networks.  In Proceedings of the ACM Web Conference 2022, pages 2501–2510, 2022.
  * [325] Fan Yang, Xiaochang Peng, Gargi Ghosh, Reshef Shilon, Hao Ma, Eider Moore, and Goran Predovic.  Exploring deep multimodal fusion of text and photo for hate speech classification.  In Proceedings of the third workshop on abusive language online, pages 11–18, 2019.
  * [326] Waheeb Yaqub, Otari Kakhidze, Morgan L Brockman, Nasir Memon, and Sameer Patil.  Effects of credibility indicators on social media news sharing intent.  In Proceedings of the 2020 chi conference on human factors in computing systems, pages 1–14, 2020.
  * [327] Wenjie Yin and Arkaitz Zubiaga.  Towards generalisable hate speech detection: a review on obstacles and solutions.  PeerJ Computer Science, 7:e598, 2021.
  * [328] Kanwal Yousaf and Tabassam Nawaz.  A deep learning-based approach for inappropriate content detection and classification of youtube videos.  IEEE Access, 10:16283–16298, 2022.
  * [329] Hua Yuan, Jie Zheng, Qiongwei Ye, Yu Qian, and Yan Zhang.  Improving fake news detection with domain-adversarial and graph-attention neural network.  Decision Support Systems, 151:113633, 2021.
  * [330] Savvas Zannettou.  ” i won the election!”: An empirical analysis of soft moderation interventions on twitter.  In Proceedings of the International AAAI Conference on Web and Social Media, volume 15, pages 865–876, 2021.
  * [331] Savvas Zannettou, Mai ElSherief, Elizabeth Belding, Shirin Nilizadeh, and Gianluca Stringhini.  Measuring and characterizing hate speech on news websites.  In 12TH ACM WEB SCIENCE CONFERENCE. ACM, 2020.
  * [332] Asta Zelenkauskaite, Pihla Toivanen, Jukka Huhtamäki, and Katja Valaskivi.  Shades of hatred online: 4chan duplicate circulation surge during hybrid media events.  First Monday, 2021.
  * [333] Justine Zhang, Ravi Kumar, Sujith Ravi, and Cristian Danescu-Niculescu-Mizil.  Conversational flow in Oxford-style debates.  In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 136–141, San Diego, California, June 2016. Association for Computational Linguistics.
  * [334] Xianchao Zhang, Shaoping Zhu, and Wenxin Liang.  Detecting spam and promoting campaigns in the twitter social network.  In 2012 IEEE 12th international conference on data mining, pages 1194–1199. IEEE, 2012.
  * [335] Yixuan Zhang, Nurul Suhaimi, Nutchanon Yongsatianchot, Joseph D Gaggiano, Miso Kim, Shivani A Patel, Yifan Sun, Stacy Marsella, Jacqueline Griffin, and Andrea G Parker.  Shifting trust: Examining how trust and distrust emerge, transform, and collapse in covid-19 information seeking.  In CHI Conference on Human Factors in Computing Systems, pages 1–21, 2022.
  * [336] Ziqi Zhang, David Robinson, and Jonathan Tepper.  Detecting hate speech on twitter using a convolution-gru based deep neural network.  In European semantic web conference, pages 745–760. Springer, 2018\.
  * [337] Zhixue Zhao, Ziqi Zhang, and Frank Hopfgartner.  A comparative study of using pre-trained language models for toxic comment classification.  In Companion Proceedings of the Web Conference 2021, pages 500–507, 2021.
  * [338] Xinyi Zhou, Atishay Jain, Vir V Phoha, and Reza Zafarani.  Fake news early detection: A theory-driven model.  Digital Threats: Research and Practice, 1(2):1–25, 2020.
  * [339] Xinyi Zhou and Reza Zafarani.  A survey of fake news: Fundamental theories, detection methods, and opportunities.  ACM Computing Surveys (CSUR), 53(5):1–40, 2020.
  * [340] C Ziems, B He, S Soni, and S Kumar.  Racism is a virus: Anti-asian hate and counterhate in social media during the covid-19 crisis. arxiv 2020.  arXiv preprint arXiv:2005.12423.
  * [341] Steven Zimmerman, Udo Kruschwitz, and Chris Fox.  Improving hate speech detection with deep learning ensembles.  In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018), 2018.

TABLE III: Community guidelines analysis of different countries. Note that _✓_ denotes that the guidelines were similar to USA and _×_ that the platform is banned in the country studied. Platforms | Germany | India | Australia | Brazil | South Africa
---|---|---|---|---|---
Parler | ✓ | ✓ | ✓ | ✓ | ✓
4chan | ✓ | ✓ | ✓ | ✓ | ✓
Gab | ✓ | ✓ | ✓ | ✓ | ✓
MeWe | ✓ | ✓ | ✓ | ✓ | ✓
Rumble | ✓ | ✓ | ✓ | ✓ | ✓
TikTok | ✓ | ×∗ | ✓ | ✓ | ✓
Twitch | ✓ | ✓ | ✓ | ✓ | ✓
Tumblr | ✓ | ✓ | ✓ | ✓ | ✓
Reddit | ✓ | ✓ | ✓ | ✓ | ✓
Snapchat | ✓ | ✓ | ✓ | ✓ | ✓
YouTube | ✓ | ✓ | ✓ | ✓ | ✓
Twitter | ✓ | ✓ | ✓ | ✓ | ✓
Facebook | ✓ | ✓ | ✓ | ✓ | ✓
Instagram | ✓ | ✓ | ✓ | ✓ | ✓

##  Appendix A Analysis of Community Guidelines of other countries:

We investigated and analyzed community guidelines of five countries, with the
highest populations in each continent i.e., India, Germany, Australia, Brazil,
and South Africa and used a VPN location, to access the community guidelines
for the platforms. We excluded China because most of the platforms
investigated in our paper are prohibited in China, and Chinese users rely on
VPNs to access these websites, causing IP addresses to be inaccurate.
Interestingly, we found no change in the guidelines. Table III shows them in
detail. TikTok was banned in India in 2020, due to a geopolitical dispute with
China [56].

##  Appendix B Related work conference list

TABLE IV: Distribution of paper analyzed by the authors Conference | Number of papers
---|---
IEEE S&P | 2
ACM CCS | 1
NDSS | 0
USENIX Security | 1
IEEE Euro S&P | 0
Proc. of ACM CSCW | 26
ACM CHI | 25
WWW | 14
ICWSM | 16
AAAI AI | 6
ACL Anthology | 37
ACM WebSci | 6
NeurIPS | 3
IEEE/CVF | 2
ACM KDD | 5
ACM WSDM | 4
ACM CIKM | 2
Other Conf. & Workshops | 35
New Media & Society | 6
Political Behaviour | 5
Other Journals | 52
Law Reviews | 7
Thesis | 1
Books | 4
Transactions | 4
Others | 9

Table IV shows the distribution of papers from various conferences and
journals that were analyzed by the authors.

##  Appendix C Related work paper list

Table LABEL:metrics-user-study shows the paper that conducted user studies to
understand moderation. In the table, we show the category of paper that was
created in Section 3, we also show which platform the authors conducted their
study on, the sample size, the demographics of the study and they type of the
study conducted. Note that in sample size, if there is a number in red color
brackets, that means that the authors of the study conducted multiple studies.
We however, present the demographics of the study with highest number of
participants.

Table VI shows the papers that are about hate speech and misinformation
detection. Table VII shows the papers that are data driven study to unpack the
intricate details about content moderation. Similar to Table LABEL:metrics-
user-study, we characterize the papers into the category, the platforms they
studied. In this table, we also give the size of the dataset, if the dataset
is public, whether the authors created the dataset or they used some other
papers dataset, the type of data used and the intervention studied. The paper
that are surrounded by red brackets, signifies that the authors studied both
hate speech and misinformation moderation, whereas, the paper that is given in
blue color signifies that that paper also conducted a user study.

TABLE V: User Study Paper Analysis  Hate Speech Moderation
---
Paper |  Categories |  Platforms |  Size |  Demographics |  Study
[267] |  Support, Removal Comprehension, Fairness and Bias |  MyVoice |  832 |  Only youth participants (14-24), 54.4% females |  Survey
[266] |  Effectiveness, Fairness and Bias |  MTurk |  573 |  Diverse demographics with transgender and non-binary participants, left-leaning (57%) |  Survey
[167] |  Effectiveness, Removal Comprehension, Fairness and Bias |  Twitter |  14 |  Mix of gender, mostly from US |  Semi-structured interviews
[77] |  Effectiveness |  HeartMob |  18 |  10 participants females, median age of participants was 40, high number of non-heterosexual participant |  Semi-structured interviews
[224] |  Effectiveness, Fairness and Bias |  Social media platforms and flyers |  49 |  Black female participants, 30 participates from ages 18-30 years |  Survey
[298] |  Effectiveness |  Residency program |  135 |  56% female, 99% created content for YouTube, 30% from the ages 35-44 |  Survey
[261] |  Effectiveness |  NGOs |  199 |  Women participants from south-east Asia, 103 from India |  Survey
[109] |  Effectiveness |  Personal contacts |  17 |  10 females, 4 LGBTQ+ participants, predominately white participants |  Survey
[138] |  Support, Removal Comprehension, Fairness and Bias |  Dynata |  2,870 |  Equal representations from US, Portugal and The Netherlands |  Survey
[323] |  Effectiveness, Support |  College students |  28 |  All participants from 18-20 years old, 18 participants were female, 23 students were of asian decent |  Interviews
Misinformation Moderation
[131] |  Consumption of (fake/misinformation) news, Engagement of user |  Facebook and Twitter |  25 |  One-third are college students, left leaning |  Semi-structured interviews and Simulation
[130] |  Consumption of (fake/misinformation) news, Support |  Facebook and Twitter |  311 |  Majority 18-24 year old (130), 38% from UK, majority left leaning |  Mixed-method survey
[121] |  Consumption of (fake/misinformation) news |  Facebook |  309 (9) |  Majority 18-25 year old (70%), 55% female, All from UK |  Pilot study and simulation
[173] |  Effectiveness |  MTurk |  238 (40) |  Two-thirds male, over half of participants from 30-49 year old (92), left-leaning (159) |  Pilot study and simulation
[162] |  Consumption of (fake/misinformation) news,Fairness and Bias |  MTurk |  1,807 |  47% left-leaning, median age 35 year old, 42% females |  Pilot Study and survey
[213] |  Engagement of users, Effectiveness |  Facebook |  501 |  51% females, majority left-leaning (47.6%), avg. age 36 year |  Survey
[97] |  Effectiveness |  Facebook |  2,994 |  Majority female (54%), majority left-leaning (58%) |  Survey
[275] |  Effectiveness |  Twitter |  319 |  56.4% males,33.3% from 25-34 age group, highly left-leaning (49.2%) |  Survey
[129] |  Effectiveness |  MTurk |  122 |  60% females, almost equal number political leaning, 80% from 25 to 54 year old |  Survey
[260] |  Effectiveness, Support, Fairness and Bias |  Dscout |  23 (15) |  Equal genders,multiple ethnicities,balanced political views |  Semi-structured interview and diary study
[228] |  Effectiveness |  Morning Consult and MTurk |  4,186 (1,546) |  54% females, largely left-leaning (49%), 42% from 18 to 34 years old |  Survey
[271] |  Effectiveness |  MTurk |  800 |  55% females, 75% participants between the age of 20 to 40 years |  Survey
[116] |  Engagement of users, Effectiveness |  Lucid |  1,473 |  Mean age of participants were 47.87, 54.1% were female |  Survey
[236] |  Engagement of users, Effectiveness |  MTurk |  5,271 (1,568) |  Female dominated (55%), largely left leaning |  Survey
[252] |  Effectiveness |  Facebook |  151 |  57% females, mean age was 25 year old |  Survey
[234] |  Engagement of users, Effectiveness |  MTurk |  11,145 |  52.9% female participants |  Pilot study and survey
[279] |  Consumption of (fake/misinformation) news |  MTurk |  1,456 (24) |  Almost equal number of male and females,large number of participants from 31-40 year of age |  Pilot study and survey
[302] |  Consumption of (fake/misinformation) news |  Twitter |  15 |  66% females, non-native English speakers |  Survey
[306] |  Consumption of (fake/misinformation) news |  WhatsApp |  28 |  53% males, average age of 32 |  Survey
[151] |  Consumption of (fake/misinformation) news |  Facebook |  519 |  74% of the participants were from the 21-30 age range,64% were students |  Survey and interviews
[199] |  Consumption of (fake/misinformation) news, Effectiveness |  WeChat |  44 |  23 were male participants, 38 partipants were residing in China |  Semi-structured interviews
[168] |  Engagement of users, Effectiveness |  MTurk |  1,677 |  Majority left-leaning 52%, majority female 55% |  Simulation
[144] |  Effectiveness |  MTurk |  376 |  57% females, 39% from the ages 18-24 years, 52% students |  Survey
[183] |  Effectiveness |  Respondi |  2,057 |  Mostly male participants |  Survey, Semi-structured interview and simulation
[272] |  Engagement of users, Effectiveness |  MTurk |  2,841 |  Mostly female (52.3%), largely between the ages of 28-37 |  Survey
[326] |  Engagement of users, Effectiveness |  MTurk |  1,512 |  mostly male participants (51%), slightly older crowd mean age 38 |  Survey
Moderation Techniques
[248] |  Support |  U.S. national panel survey |  1,022 |  53.2% female, 75% white ethnicity, 38.4% from the ages 30-49 |  Survey
[225] |  Support, Removal Comprehension, Fairness and Bias |  OnlineCensors hip.org |  519 |  Participants largely from US (295) |  Survey
[164] |  Effectiveness, Support, Removal Comprehension, Fairness and Bias |  Reddit |  907 |  Participants from 81 countries, highest from US (61%), majority male (81%), under 25 year old (55%) |  Survey
[149] |  Removal Comprehension, Fairness and Bias |  Prolific, Qualtrics |  909 (207) |  Mixed genders, balanced ethnicity’s, largely young, mix of conservatives and moderates |  Survey
[270] |  Removal Comprehension |  Twitch, Reddit, Facebook |  56 |  Largly female and LGBTQ participants |  Semi-structured interviews
[117] |  Removal Comprehension |  Yelp |  15 |  40% were between ages 35-44 |  Survey
[75] |  Consumption of (fake/misinformation) news |  Twitter |  430 (12) |  Slightly skewed towards females, higher number of independents and republicans |  Pilot Study and Simulation
[200] |  Consumption of (fake/misinformation) news |  WeChat |  33 |  More number of females (18), average age of 34 |  Semi-structured interviews
[335] |  Consumption of (fake/misinformation) news |  Qualtrics |  177 (21) |  Largely female (66%), between the ages of 25-34 |  Pilot study and survey
[172] |  Removal Comprehension, Fairness and Bias |  Reddit |  13 |  70% male population, 2 people from other countries other than US |  Interviews
[96] |  Support |  Prolific, Qualtrics, Embrain |  5,392 |  Diverse population from US, UK, South Korea and Mexico, female dominated study (52.35%), older participants (median age = 41) |  Survey
[111] |  Support, Removal Comprehension, Fairness and Bias |  TikTok, YouTube, Twitch |  30 |  All participants had historically marginalized identities |  Semi-structured interviews
[201] |  Support, Fairness and Bias |  MTurk |  100 |  62% male participants, all from USA, older participants (avg. age = 37) |  Survey
[296] |  Removal Comprehension, Fairness and Bias |  OnlineCensors hip.org |  380 |  N/A |  Survey
[78] |  Support, Fairness and Bias |  Twitter, Instagram |  262 |  Females, 38.9% were sex workers and activists |  Survey
TABLE VI: Papers about Detection Techniques

Hate Speech Detection
---
Categories |  Broad Description |  Papers
Content based |  What type of features (i.e., TF-IDF, BoWV, POS tags etc.) can we obtain solely from the content to detect hate speech? |  [255, 101, 258, 102]
Lexicon Based |  How can we utilize the syntactic and semantic orientations of the existing lexicons to identify hate speech? |  [126, 310, 87, 263, 72, 297, 307, 205, 115, 321]
Deep Neural Based |  How can we use the advancements in Deep Neural Networks to identify hate speech in platforms without using the handcrafted features ? |  [328, 127, 65, 95, 336, 237, 59, 146, 337, 206, 110, 247, 137, 317, 175, 253, 177, 123, 233, 70, 132, 114, 292, 305, 71, 245, 60, 242, 58, 340, 192, 308, 217, 229, 221, 61, 256, 88, 106, 82, 197, 322, 124, 160, 220]
Hybrid Approaches |  How can we effectively combine content based, lexicon based and deep neural based methods to identify hate speech? |  [103, 128, 259, 208, 94, 235, 63]
Multi-modal Based |  How can we combine multiple modalities of posts (such as images, texts, memes) together to identify hate speech? |  [325, 285, 137, 179, 239, 194, 309, 311]
Misinformation Detection
Content Based |  What type of features (i.e., TF-IDF, BoWV, POS tags etc.) can we obtain solely from the content to detect misinformation? |  [287, 338, 154, 104, 156, 238, 243, 313, 314, 119, 150]
Propagation Structure |  How can we detect posts that contain misinformation by mining their spreading patterns in the underlying social networks? |  [318, 262, 281, 195, 216, 287, 282, 284]
Hybrid Approaches |  How can we effectively combine both content based features and propagation structure to detect misinformation? |  [254, 283, 170, 176, 153]
Crowd Intelligence |  Can we use the wisdom of the crowd to identify misinformation on the platform? |  [181, 202, 301, 312, 171, 241, 280]
Deep Neural Based |  How can we use the advancements in Deep Neural Networks to identify misinformation in platforms without using the handcrafted features ? |  [64, 246, 76, 329, 202, 280, 196, 254, 219, 315, 174, 291, 211, 324, 100, 198, 318, 341]
Knowledge Based |  Can we effectively use Knowledge Graphs to identify and automatically detect posts that contains misinformation? |  [157, 185, 100, 112, 231, 291, 211, 324]
Multi-modal Based |  How can we combine multiple modalities of posts (such as images, texts, and audio) together to identify misinformation? |  [288, 178, 315, 320, 240, 274, 290]

TABLE VII: Data Driven Study Paper Analysis

Hate Speech Moderation
---
Paper |  Category |  Platforms |  Size of Dataset |  Is Dataset Public? |  Created the dataset? |  Type of Data used |  Intervention Studied
[90] |  Engagement of users, Effectiveness |  Reddit |  $\sim$85M |  No |  Yes |  Posts and metadata |  Soft Moderation
[155] |  Engagement of users, Effectiveness |  Reddit |  $\sim$6.3M |  Yes |  Partially |  Comments, posts and metadata |  Hard Moderation
[91] |  Engagement of users, Effectiveness |  Reddit |  $\sim$100M |  Partially |  No |  Comments and posts |  Hard Moderation
[62] |  Engagement of users, Effectiveness |  Gab, Reddit and Twitter |  $\sim$30M |  Partially |  Partially |  Posts and metadata |  Hard Moderation
[166] |  Engagement of users, Effectiveness |  Twitter |  $\sim$49M |  No |  Yes |  Tweets and metadata |  Hard Moderation
[257] |  Engagement of users, Effectiveness |  Reddit |  $\sim$1.9M |  Partially |  No |  Comments and metadata |  Hard Moderation
[92] |  Effectiveness |  Reddit |  $\sim$2.8M |  No |  Yes |  Comments |  Hard Moderation
[223] |  Effectiveness |  Twitter |  N/A |  No |  Yes |  Tweets and metadata |  Hard Moderation
[300] |  Engagement of users, Effectiveness |  Reddit |  $\sim$15M |  Yes |  No |  Posts |  Soft Moderation
[244] |  Engagement of users, Effectiveness |  YouTube and BitChute |  $\sim$11K |  No |  Partially |  Videos and metadata |  Hard Moderation
[251] |  Effectiveness |  Telegram |  N/A |  No |  Yes |  Posts and metadata |  Hard Moderation
[188] |  Effectiveness |  Parler |  $\sim$200M |  Partially |  Partially |  Posts and metadata |  Hard Moderation
[278] |  Engagement of users, Effectiveness |  Reddit |  $\sim$3.7M |  Partially |  No |  Posts, metadata |  Soft Moderation
Misinformation Moderation
[330] |  Engagement of users |  Twitter |  $\sim$18K |  Yes |  Yes |  Tweets and metadata |  Soft Moderation
[193] |  Engagement of users |  TikTok |  $\sim$41K |  No |  Yes |  Videos and metadata |  Soft Moderation
[182] |  Engagement of users, Effectiveness |  YouTube |  105 |  No |  Yes |  Videos metadata |  Soft Moderation
[232] |  Engagement of users, Effectiveness |  Twitter |  $\sim$2.4M |  Yes |  Yes |  Tweets and metadata |  Soft Moderation
Moderation Techniques
[277] |  Fairness and Bias |  Reddit |  $\sim$9K |  Yes |  No |  Posts |  Soft Moderation
[299] |  Effectiveness |  Reddit |  N/A |  Partially |  No |  Posts and comments |  Hard Moderation
[172] |  Removal Comprehension, Fairness and Bias |  Reddit |  $\sim$0.5M |  No |  Yes |  Moderation logs |  Hard Moderation