SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice

The Discovery of Grounded Theory: Strategies for Qualitative Research

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Language Models are Few-Shot Learners

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RoBERTa: A Robustly Optimized BERT Pretraining Approach

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The online disinhibition effect

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Snowball Sampling

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Automated Hate Speech Detection and the Problem of Offensive Language

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The politics of ‘platforms’

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European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”

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"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection

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Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

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A Survey on Hate Speech Detection using Natural Language Processing

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Proceedings of the 28th International Conference on Computational Linguistics

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Adversarial Examples for Evaluating Reading Comprehension Systems

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Deep Learning for Hate Speech Detection in Tweets

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A Survey on Automatic Detection of Hate Speech in Text

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A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities

Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184

EANN

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Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Algorithmic content moderation: Technical and political challenges in the automation of platform governance

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Proceedings of the ACM on Human-Computer Interaction

Karahalios, K. G., Monroy‐Hernández, A., Lampinen, A., & Fitzpatrick, G. (2018) · Cited by 1,070

CSI: A Hybrid Deep Model for Fake News Detection

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Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking

Rashkin, H., Choi, E., Jang, J. Y., Volkova, S., & Choi, Y. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1317 · Cited by 965

Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter

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SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter

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Assessing the Effect of 'Disputed' Warnings and Source Salience on Perceptions of Fake News Accuracy

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Debunking: A Meta-Analysis of the Psychological Efficacy of Messages Countering Misinformation

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Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs

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FakeBERT: Fake news detection in social media with a BERT-based deep learning approach

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MVAE: Multimodal Variational Autoencoder for Fake News Detection

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The Risk of Racial Bias in Hate Speech Detection

Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1163 · Cited by 817

Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media

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Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

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This Just In: Fake News Packs A Lot In Title, Uses Simpler, Repetitive Content in Text Body, More Similar To Satire Than Real News

Horne, B. D., & Adalı, S. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14976 · Cited by 787

Spotting fake reviewer groups in consumer reviews

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What is platform governance?

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A Stylometric Inquiry into Hyperpartisan and Fake News

Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., & Stein, B. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1022 · Cited by 700

Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network

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Rumor Detection on Twitter with Tree-structured Recursive Neural Networks

Ma, J., Gao, W. G., & Wong, K. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1184 · Cited by 675

Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks

Liu, Y., & Wu, Y.-F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11268 · Cited by 663 · cited 2×

Real Solutions for Fake News? Measuring the Effectiveness of General Warnings and Fact-Check Tags in Reducing Belief in False Stories on Social Media

Clayton, K., Blair, S., Busam, J. A., Forstner, S., Glance, J., Green, G., Kawata, A., Kovvuri, A., Martin, J., Morgan, E., et al. (2020) · Political Behavior · doi:10.1007/s11109-019-09533-0 · Cited by 658

dEFEND

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Ex Machina: Personal Attacks Seen at Scale

Wulczyn, E., Thain, N., & Dixon, L. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052591 · Cited by 636

Beyond News Contents

Shu, K., Wang, S., & Liu, H. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3290994 · Cited by 630

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Beede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P., & Vardoulakis, L. M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376718 · Cited by 614

Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior

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The New Governors: The People, Rules, and Processes Governing Online Speech

Klonick, K. (2018) · St. John's University School of Law (St. John's University) · Cited by 567

Using Convolutional Neural Networks to Classify Hate-Speech

Gambäck, B., & Sikdar, U. K. (2017) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w17-3013 · Cited by 561

What is a flag for? Social media reporting tools and the vocabulary of complaint

Crawford, K., & Gillespie, T. (2016) · New Media & Society · doi:10.1177/1461444814543163 · Cited by 496

Deplatforming: Following extreme Internet celebrities to Telegram and alternative social media

Rogers, R. (2020) · European Journal of Communication · doi:10.1177/0267323120922066 · Cited by 451

Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms

West, S. M. (2018) · New Media & Society · doi:10.1177/1461444818773059 · Cited by 445

Platform governance at the margins: Social media creators’ experiences with algorithmic (in)visibility

Duffy, B. E., & Meisner, C. (2022) · Media Culture & Society · doi:10.1177/01634437221111923 · Cited by 440

An ensemble machine learning approach through effective feature extraction to classify fake news

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Effective hate-speech detection in Twitter data using recurrent neural networks

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Hate Speech Dataset from a White Supremacy Forum

de Gibert, O., Pérez, N., Pablos, A. G., & Cuadros, M. (2018) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w18-5102 · Cited by 428

GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

Lu, Y.-J., & Li, C.-T. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.48 · Cited by 427

Taking Fact-Checks Literally But Not Seriously? The Effects of Journalistic Fact-Checking on Factual Beliefs and Candidate Favorability

Nyhan, B., Porter, E., Reifler, J., & Wood, T. (2019) · Political Behavior · doi:10.1007/s11109-019-09528-x · Cited by 413

One-step and Two-step Classification for Abusive Language Detection on Twitter

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Mean Birds

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An exploratory study of COVID-19 misinformation on Twitter

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A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

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Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment

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Fake news detection on social media using geometric deep learning

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You Can't Stay Here: The Efficacy of Reddit's 2015 Ban Examined Through Hate Speech

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Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms

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News Verification by Exploiting Conflicting Social Viewpoints in Microblogs

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Understanding User Profiles on Social Media for Fake News Detection

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HateBERT: Retraining BERT for Abusive Language Detection in English

Caselli, T., Basile, V., Mitrović, J., & Granitzer, M. (2021) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2021.woah-1.3 · Cited by 373

Multimodal Fusion with Co-Attention Networks for Fake News Detection

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Hate Me, Hate Me Not: Hate Speech Detection on Facebook.

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Exploiting Multi-domain Visual Information for Fake News Detection

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Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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The hateful memes challenge: Detecting hate speech in multimodal memes

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Moderator engagement and community development in the age of algorithms

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Disproportionate Removals and Differing Content Moderation Experiences for Conservative, Transgender, and Black Social Media Users: Marginalization and Moderation Gray Areas

Haimson, O. L., Delmonaco, D., Nie, P., & Wegner, A. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479610 · Cited by 343

Toward Automated Fact-Checking

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Multilingual and Multi-Aspect Hate Speech Analysis

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Deep learning for misinformation detection on online social networks: a survey and new perspectives

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Developing an online hate classifier for multiple social media platforms

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Hate Lingo: A Target-Based Linguistic Analysis of Hate Speech in Social Media

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Detecting Hate Speech in Social Media

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Human-Machine Collaboration for Content Regulation: The Case of Reddit Automoderator

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Exploring Hate Speech Detection in Multimodal Publications

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Tiered Governance and Demonetization: The Shifting Terms of Labor and Compensation in the Platform Economy

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The Internet's Hidden Rules: An Empirical Study of Reddit Norm Violations at Micro, Meso, and Macro Scales

Chandrasekharan, E., Samory, M., Jhaver, S., Charvat, H., Bruckman, A., Lampe, C., Eisenstein, J., & Gilbert, E. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274301 · Cited by 282

Challenges in discriminating profanity from hate speech

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Detecting Online Hate Speech Using Context Aware Models

Gao, L., & Huang, R. (2017) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-049-6_036 · Cited by 273

Fake News Early Detection

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Online Harassment and Content Moderation: The Case of Blocklists

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The role of user profiles for fake news detection

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Convolutional Neural Networks for Toxic Comment Classification

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A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks

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Hate speech detection and racial bias mitigation in social media based on BERT model

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“I agree to the terms and conditions”: (How) do users read privacy policies online? An eye-tracking experiment

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A Rape in Cyberspace; or, How an Evil Clown, a Haitian Trickster Spirit, Two Wizards, and a Cast of Dozens Turned a Database into a Society

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Cleaning Up Social Media: The Effect of Warning Labels on Likelihood of Sharing False News on Facebook

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Classification and Its Consequences for Online Harassment: Design Insights from HeartMob

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Towards generalisable hate speech detection: a review on obstacles and solutions

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Neural User Response Generator: Fake News Detection with Collective User Intelligence

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A Unified Deep Learning Architecture for Abuse Detection

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Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge

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What is so special about online (as compared to offline) hate speech?

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Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

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Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

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Fake News Detection in Social Networks via Crowd Signals

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Automatic Online Fake News Detection Combining Content and Social Signals

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Content Based Fake News Detection Using Knowledge Graphs

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Weakly Supervised Learning for Fake News Detection on Twitter

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Falling for Fake News: Investigating the Consumption of News via Social Media

Flintham, M., Karner, C., Bachour, K., Creswick, H., Gupta, N., & Moran, S. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173950 · Cited by 194

MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets

Pramanick, S., Sharma, S., Dimitrov, D., Akhtar, M. S., Nakov, P., & Chakraborty, T. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.379 · Cited by 191

The Virtues of ModerationPreprint

Grimmelmann, J. (2017) · doi:10.31228/osf.io/qwxf5 · Cited by 187

Detecting weak and strong Islamophobic hate speech on social media

Vidgen, B., & Yasseri, T. (2019) · Journal of Information Technology & Politics · doi:10.1080/19331681.2019.1702607 · Cited by 185

User attitudes towards algorithmic opacity and transparency in online reviewing platforms

Eslami, M., Vaccaro, K., Lee, M. K., Elazari Bar On, A., Gilbert, E., & Karahalios, K. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300724 · Cited by 184

Toxic Talk: How Online Incivility Can Undermine Perceptions of Media

Anderson, A., Yeo, S. K., Brossard, D., Scheufele, D. A., & Xenos, M. A. (2016) · International Journal of Public Opinion Research · doi:10.1093/ijpor/edw022 · Cited by 182

Designing Cyberbullying Mitigation and Prevention Solutions through Participatory Design With Teenagers

Ashktorab, Z., & Jessica Vitak, B. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858548 · Cited by 179

FNED

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DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation

Cui, L., Seo, H., Tabar, M., Ma, F., Wang, S., & Lee, D. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403092 · Cited by 177

An Italian Twitter Corpus of Hate Speech against Immigrants

Sanguinetti, M., Poletto, F., Bosco, C., Patti, V., & Stranisci, M. A. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/26vnss59hfcq · Cited by 176

"They Don't Leave Us Alone Anywhere We Go": Gender and Digital Abuse in South Asia

Sambasivan, N., Batool, A., Ahmed, N., Matthews, T., Thomas, K., Gaytán-Lugo, L. S., Nemer, D., Bursztein, E., Churchill, E., & Consolvo, S. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300232 · Cited by 175

Reconsidering Self-Moderation

Seering, J. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415178 · Cited by 174

Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media

Salminen, J. O., Almerekhi, H., Milenković, M., Jung, S., An, J., Kwak, H., & Jansen, B. J. J. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.15028 · Cited by 173

Effects of Credibility Indicators on Social Media News Sharing Intent

Yaqub, W., Kakhidze, O., Brockman, M. L., Memon, N., & Patil, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376213 · Cited by 171 · cited 4×

Temporally evolving graph neural network for fake news detection

Song, C., Shu, K., & Wu, B. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102712 · Cited by 170

"Did You Suspect the Post Would be Removed?"

Jhaver, S., Appling, D. S., Gilbert, É., & Bruckman, A. S. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359294 · Cited by 170

Fake News on Facebook and Twitter: Investigating How People (Don't) Investigate

Geeng, C., Yee, S., & Roesner, F. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376784 · Cited by 169

Evaluating the Effectiveness of Deplatforming as a Moderation Strategy on Twitter

Jhaver, S., Boylston, C., Yang, D., & Bruckman, A. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479525 · Cited by 169

Thou Shalt Not Hate: Countering Online Hate Speech

Mathew, B., Saha, P., Tharad, H., Rajgaria, S., Singhania, P., Maity, S. K., Goyal, P., & Mukherjee, A. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3237 · Cited by 166

Hate Speech Detection is Not as Easy as You May Think

Arango, A., Pérez, J. E. P., & Poblete, B. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331262 · Cited by 165

Multi-Source Multi-Class Fake News Detection

Karimi, H. R., Roy, P. C., Saba-Sadiya, S., & Tang, J. (2018) · International Conference on Computational Linguistics · Cited by 156

The Bag of Communities

Chandrasekharan, E., Samory, M., Srinivasan, A., & Gilbert, É. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3026018 · Cited by 148

Towards understanding and detecting fake reviews in app stores

Martens, D., & Maalej, W. (2019) · Empirical Software Engineering · doi:10.1007/s10664-019-09706-9 · Cited by 145

Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation

Lai, V., Carton, S., Bhatnagar, R., Liao, Q. V., Zhang, Y., & Tan, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501999 · Cited by 145

A Deep Learning-Based Approach for Inappropriate Content Detection and Classification of YouTube Videos

Yousaf, K., & Nawaz, T. (2022) · IEEE Access · doi:10.1109/access.2022.3147519 · Cited by 142

A Note on the Interpretation of Weighted Kappa and its Relations to Other Rater Agreement Statistics for Metric Scales

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The Rise of Guardians

Vo, N., & Lee, K. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210037 · Cited by 137

Online Hate Speech against Women: Automatic Identification of Misogyny and Sexism on Twitter

Frenda, S., Ghanem, B., Montes-y-Gómez, M., & Rosso, P. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-179023 · Cited by 137

Evidence-aware Fake News Detection with Graph Neural Networks

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KAN: Knowledge-aware Attention Network for Fake News Detection

Dun, Y., Tu, K., Chen, C., Hou, C., & Yuan, X. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16080 · Cited by 131

Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit

Chandrasekharan, E., Jhaver, S., Bruckman, A., & Gilbert, E. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3490499 · Cited by 129

Digital detritus: 'Error' and the logic of opacity in social media content moderation

Roberts, S. (2018) · First Monday · doi:10.5210/fm.v23i3.8283 · Cited by 127

Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels

Horta Ribeiro, M., Jhaver, S., Zannettou, S., Blackburn, J., Stringhini, G., De Cristofaro, E., & West, R. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476057 · Cited by 124

Detecting White Supremacist Hate Speech Using Domain Specific Word Embedding With Deep Learning and BERT

Alatawi, H. S., Alhothali, A., & Moria, K. M. (2021) · IEEE Access · doi:10.1109/access.2021.3100435 · Cited by 121

Misinformation warnings: Twitter’s soft moderation effects on COVID-19 vaccine belief echoes

Sharevski, F., Alsaadi, R., Jachim, P., & Pieroni, E. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102577 · Cited by 120

Machine Learning in Adversarial Settings

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Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes ChallengePreprint

Velioğlu, R., & Rose, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12975 · Cited by 116

A Multimodal Framework for the Detection of Hateful MemesPreprint

Lippe, P., Holla, N., Chandra, S., Rajamanickam, S., Antoniou, G., Shutova, E., & Yannakoudakis, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12871 · Cited by 116

Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media

Jahanbakhsh, F., Zhang, A. X., Berinsky, A. J., Pennycook, G., Rand, D. G., & Karger, D. R. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449092 · Cited by 112

Understanding the Effect of Deplatforming on Social Networks

Ali, S., Saeed, M. H., Aldreabi, E., Blackburn, J., De Cristofaro, E., Zannettou, S., & Stringhini, G. (2021) · 13th ACM Web Science Conference 2021 · doi:10.1145/3447535.3462637 · Cited by 109 · cited 4×

Improving fake news detection with domain-adversarial and graph-attention neural network

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Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)

Kumar, R., Ojha, A. K., Zampieri, M., & Malmasi, S. (2018) · Cited by 108

A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok

Shang, L., Kou, Z., Zhang, Y., & Wang, D. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671928 · Cited by 107

Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities

Abdali, S., Shaham, S., & Krishnamachari, B. (2024) · ACM Computing Surveys · doi:10.1145/3697349 · Cited by 107

Evaluating the legitimacy of platform governance: A review of research and a shared research agenda

Suzor, N. P., Geelen, T. V., & West, S. M. (2018) · International Communication Gazette · doi:10.1177/1748048518757142 · Cited by 106

Regulating Online Content Moderation

Langvardt, K. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3024739 · Cited by 103

"At the End of the Day Facebook Does What ItWants": How Users Experience Contesting Algorithmic Content Moderation

Vaccaro, K., Sandvig, C., & Karahalios, K. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415238 · Cited by 102

Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels

Ribeiro, M. H., Jhaver, S., Zannettou, S., Blackburn, J., De Cristofaro, E., Stringhini, G., & West, R. (2020) · arXiv · doi:10.48550/arxiv.2010.10397 · Cited by 102

Detecting Spam and Promoting Campaigns in the Twitter Social Network

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Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets

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Combating Misinformation in Bangladesh

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An Overview of Perceptual Hashing

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Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations

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Toward Multimodal Cyberbullying Detection

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Characterizing COVID-19 Misinformation Communities Using a Novel Twitter DatasetPreprint

Memon, S. A., & Carley, K. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00791 · Cited by 96

Leveraging the Implicit Structure within Social Media for Emergent Rumor Detection

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Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification

Yang, F., Peng, X., Ghosh, G., Shilon, R., Ma, H., Moore, E., & Predović, G. (2019) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w19-3502 · Cited by 94

Countering Fake News

Kirchner, J., & Reuter, C. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415211 · Cited by 91

Experiences of Harm, Healing, and Joy among Black Women and Femmes on Social Media

Musgrave, T., Cummings, A., & Schoenebeck, S. Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517608 · Cited by 91

The algorithmic dance: YouTube’s Adpocalypse and the gatekeeping of cultural content on digital platforms

Kumar, S. (2019) · Internet Policy Review · doi:10.14763/2019.2.1417 · Cited by 89

Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media

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Detecting Spam URLs in Social Media via Behavioral Analysis

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Improving Hate Speech Detection with Deep Learning Ensembles

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Empirical Analysis of Multi-Task Learning for Reducing Identity Bias in Toxic Comment Detection

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Leveraging Intra and Inter Modality Relationship for Multimodal Fake News Detection

Singhal, S., Pandey, T., Mrig, S., Shah, R. R., & Kumaraguru, P. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524650 · Cited by 86

Drawing from justice theories to support targets of online harassment

Schoenebeck, S., Haimson, O. L., & Nakamura, L. (2021) · New Media & Society · doi:10.1177/1461444820913122 · Cited by 84

Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection

Ren, Y., Wang, B., Zhang, J., & Chang, Y. (2020) · Industrial Conference on Data Mining · doi:10.1109/icdm50108.2020.00054 · Cited by 84

Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security

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FERMI at SemEval-2019 Task 5: Using Sentence embeddings to Identify Hate Speech Against Immigrants and Women in Twitter

Indurthi, V., Syed, B., Shrivastava, M., Chakravartula, N., Gupta, M., & Varma, V. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2009 · Cited by 83

Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours

Sharma, K., Zhang, Y., Ferrara, E., & Liu, Y. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467391 · Cited by 83

NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges

Bhuiyan, M. M., Horning, M., Lee, S. W., & Mitra, T. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479571 · Cited by 83

Hate Speech on Social Media: Content Moderation in Context

Wilson, R. A., & Land, M. K. (2021) · OpenCommons at University of Connecticut (University of Connecticut) · Cited by 83

Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 CrisisPreprint

Ziems, C., He, B., Soni, S., & Kumar, S. (2020) · arXiv (Cornell University) · Cited by 81

Comparing the Perceived Legitimacy of Content Moderation Processes: Contractors, Algorithms, Expert Panels, and Digital Juries

Pan, C. A., Yakhmi, S., Iyer, T., Strasnick, E., Zhang, A. X., & Bernstein, M. S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512929 · Cited by 75

"I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter

Zannettou, S. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18110 · Cited by 74

Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection

Nozza, D. (2021) · doi:10.18653/v1/2021.acl-short.114 · Cited by 74

Mining Misinformation in Social Media

Wu, L., Morstatter, F., Hu, X., & Liu, H. (2016) · doi:10.1201/9781315396705-14 · Cited by 74

Antecedents of support for social media content moderation and platform regulation: the role of presumed effects on self and others

Riedl, M. J., Whipple, K. N., & Wallace, R. (2021) · Information Communication & Society · doi:10.1080/1369118x.2021.1874040 · Cited by 73

All You Need is "Love": Evading Hate-speech DetectionPreprint

Gröndahl, T., Pajola, L., Juuti, M., Conti, M., & Asokan, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09115 · Cited by 73

To Label or Not to Label

Gao, M., Xiao, Z., Karahalios, K. G., & Fu, W. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274324 · Cited by 70

Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning ModelsPreprint

Satyajit, K., & Joshi, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.05145 · Cited by 70

An automated text categorization framework based on hyperparameter optimization

Téllez, E. S., Moctezuma, D., Miranda‐Jiménez, S., & Graff, M. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.03.003 · Cited by 70

“It’s common and a part of being a content creator”: Understanding How Creators Experience and Cope with Hate and Harassment Online

Thomas, K., Kelley, P. G., Consolvo, S., Samermit, P., & Bursztein, E. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501879 · Cited by 70

Trust It or Not

Seo, H., Xiong, A., & Lee, D. (2019) · ACM Web Science Conference (WebSci) · doi:10.1145/3292522.3326012 · Cited by 67

Measuring and Characterizing Hate Speech on News Websites

Zannettou, S., ElSherief, M., Belding, E., Nilizadeh, S., & Stringhini, G. (2020) · ACM Web Science Conference (WebSci) · doi:10.1145/3394231.3397902 · Cited by 67

Monetizing a Meme: YouTube, Content ID, and the Harlem Shake

Soha, M., & McDowell, Z. J. (2016) · Social Media + Society · doi:10.1177/2056305115623801 · Cited by 66

Do Explanations Increase the Effectiveness of AI-Crowd Generated Fake News Warnings?

Epstein, Z., Foppiani, N., Hilgard, S., Sharma, S., Glassman, E. L., & Rand, D. G. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19283 · Cited by 66

Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald

Trujillo, A., & Cresci, S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555639 · Cited by 65

Misleading or Falsification

Volkova, S., & Jang, J. Y. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3188728 · Cited by 63

SoK: Content Moderation for End-to-End Encryption

Scheffler, S., & Mayer, J. R. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0060 · Cited by 43

"Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies

Schaffner, B., Bhagoji, A. N., Cheng, S., Mei, J., Shen, J., Wang, G., Chetty, M., Feamster, N., Lakier, G., & Tan, C. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642333 · Cited by 24

Children, Parents, and Misinformation on Social Media

Sharevski, F., & Loop, J. V. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00221 · Cited by 16

Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models

Palla, K., Redondo-García, J. L., Hauff, C., Fabbri, F., Damianou, A., Lindström, H., Taber, D., & Lalmas, M. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732054 · Cited by 41

Cybersecurity Misinformation Detection on Social Media: Case Studies on Phishing Reports and Zoom’s Threat

Singhal, M., Kumarswamy, N., Kinhekar, S., & Nilizadeh, S. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22189 · Cited by 15

Crowdsourcing the Mitigation of disinformation and misinformation: The case of spontaneous community-based moderation on Reddit

Corsi, G., Seger, E., & hÉigeartaigh, S. Ó. (2024) · Online Social Networks and Media · doi:10.1016/j.osnem.2024.100291 · Cited by 14

One of Many: Assessing User-level Effects of Moderation Interventions on r/The_Donald

Trujillo, A., & Cresci, S. (2023) · ACM Web Science Conference (WebSci) · doi:10.1145/3578503.3583626 · Cited by 13

Supporting Human Raters with the Detection of Harmful Content Using Large Language Models

Thomas, K., Kelley, P. G., Tao, D. D. J., Meiklejohn, S., Vallis, O., Tan, S., Bratanič, B., Ferreira, F. T., Eranti, V., & Bursztein, E. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00082 · Cited by 10

Bans vs. Warning Labels: Examining Bystanders’ Support for Community-wide Moderation Interventions

Jhaver, S. (2025) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3715116 · Cited by 8

Consensus on community guidelines: an experimental study on the legitimacy of content removal in social media

Aguerri, J. C., Llinares, F. M., & Gómez-Bellvís, A. B. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01917-2 · Cited by 9

Exploring the Distinctive Tweeting Patterns of Toxic Twitter Users

Qayyum, H., Ikram, M., Zhao, B. Z. H., Wood, I. D., Kourtellis, N., & Kâafar, M. A. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386402 · Cited by 5

Detection of Cyberbullying on Social Media Platforms Using Machine Learning

Ali, M. U., & Lefticaru, R. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-47508-5_18 · Cited by 5

Bystander Privacy in Video Sharing Era: Automated Consent Compliance through Platform Censorship

Liao, S., He, H., Chen, H., & Yang, Z. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713391 · Cited by 4

Evaluating the robustness of fake news detectors to adversarial attacks with real user comments

Koren, A., Underwood, C., Serra, E., & Spezzano, F. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00790-3 · Cited by 4

Beyond trial-and-error: Predicting user abandonment after a moderation intervention

Tessa, B., Cima, L., Trujillo, A., Avvenuti, M., & Cresci, S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112375 · Cited by 4

No Easy Way Out: the Effectiveness of Deplatforming an Extremist Forum to Suppress Hate and Harassment

Vu, A. V., Hutchings, A., & Anderson, R. (2023) · IEEE Symposium on Security and Privacy · doi:10.1109/sp54263.2024.00007 · Cited by 15

Verifying the robustness of automatic credibility assessment

Przybyła, P., Shvets, A., & Saggion, H. (2024) · Natural language processing. · doi:10.1017/nlp.2024.54 · Cited by 10

Measurement and Metrics for Content Moderation

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How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape

Kelley, P. G., Rousso-Schindler, S., Shelby, R. M., Thomas, K., & Woodruff, A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791363 · Cited by 3

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Between Online and Offline Due Process: The Digital Services Act

Gentile, G. (2024) · European Union and its neighbours in a globalized world · doi:10.1007/978-3-031-65381-0_11 · Cited by 3

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Bäumler, J., Bader, H., Kaufhold, M., & Reuter, C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713542 · Cited by 5

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Causal Insights into Parler's Content Moderation Shift: Effects on Toxicity and Factuality

Kumarswamy, N., Singhal, M., & Nilizadeh, S. (2025) · The Web Conference · doi:10.1145/3696410.3714865 · Cited by 7

Navigating the Gray Interventions: The Impact of Soft Moderation Techniques Across Social Media Platforms

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Febryani, E. (2025) · The Easta Journal Law and Human Rights · doi:10.58812/eslhr.v3i03.639 · Cited by 1

BERT-OTA: Enhancing Hate Speech Detection With Ontology-Guided Transformer Attention

Abusaqer, M., Saquer, J. M., & Ghosh, M. (2026) · IEEE Access · doi:10.1109/access.2026.3650874 · Cited by 3

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Sharevski, F., Loop, J. V., Devine, A., Jachim, P., & Das, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790857 · Cited by 1

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Lee, D., Ju, J., Song, H., & Park, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791249 · Cited by 2

On mission Twitter Profiles: A Study of Selective Toxic Behavior

Qayyum, H., Ikram, M., Zhao, B. Z. H., Wood, I. D., Kourtellis, N., & Kâafar, M. A. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386248 · Cited by 1

CART: A Tool for Making Paper Relevancy Screening Easier

Reitinger, N. (2024) · The Journal of Open Source Software · doi:10.21105/joss.06494 · Cited by 1

AI Powered Video Content Moderation Governed by Intensity Based Custom Rules with Remedial Pipelines

Vamsikeshwaran, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93691-3_29 · Cited by 1

Accountability Gaps in the Fake Follower Market

Kalameyets, M., Owens, R., Hammouchi, H., Sergeeva, A., Hoehn, S., Jalilzade, E., Wang, J., Patel, S., Aidinlis, S., Farrand, B., et al. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3806758

SocialEcho: A Social Networking Platform with Community Guidelines Violation Pre-check

Mahmud, N., Emon, M. I. H., Beg, M. M. H., & Islam, M. M. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-50215-6_14

Trust and Safety Risks and Engineering Patterns Through the Lens of Issue Resolution: An Empirical Case Study in Mastodon and DiasporaPreprint

Cramer, G., Maxam, W. P., & Davis, J. C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4768911

Digital Sovereignty and State Power: Indonesia’s Approach to Digital Platforms Regulation

Gumati, M. R. (2024) · JISPO Jurnal Ilmu Sosial dan Ilmu Politik · doi:10.15575/jispo.v14i1.39500

Knowledge-Graph NLP Assistant for Mitigate Norm Violations: Live Chat Moderation Study

Mulyadi, H. B., Sarno, R., Septiyanto, A. F., & Sunaryono, D. (2024) · Beyond Technology Summit on Informatics International Conference (BTS-I2C) · doi:10.1109/bts-i2c63534.2024.10942255

Understanding and Deobfuscating Textual Data: Managerial Insights and Computational Challenges

Shapiro, S., & Lam, V. (2025) · IEEE Engineering Management Review · doi:10.1109/emr.2025.3567192

Platforms on the hook? EU and human rights requirements for human involvement in content moderation

Penagos, E. V. (2025) · Cambridge Forum on AI Law and Governance · doi:10.1017/cfl.2025.3

ContentGuard: A Dual-Channel AI System for Filtering Unsafe Text and Images

Verma, L., Sarak, R., Nehe, Y., & Shete, R. (2025) · International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) · doi:10.1109/i2itcon65200.2025.11210490

Toxic Ink on Immutable Paper: Content Moderation for Ethereum Input Data Messages (IDMs)

Xiong, X., Wang, Z., Wang, Q., & Knottenbelt, W. J. (2025) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom66490.2025.00196

Adapting the Dangerous Speech Paradigm to Identify Incitement from Polarized and Hate Speech

Patel, S., Faklaris, C., Mahiyat, R., Sampath, A. N., & Krishnan, S. (2026) · ACM Transactions on Social Computing · doi:10.1145/3797963

Conflicting Rationales, Shifting Stances: Unpacking User Divergence in Online Geopolitical Debates

He, Y.-P., Zhang, P., Haq, E.-U., He, J.-H., & Tyson, G. (2026) · International Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790382 · Cited by 1

Bystanders and Reporters: Who Acts Against Illegal Online Content?

Quint, F., Theocharis, Y., Kosmidis, S., & Roberts, M. E. (2026) · Social Media + Society · doi:10.1177/20563051261437497

Situational crime prevention and child online safety: a comparative analysis of metaverse-ready games

Cavaioni, L., Fuller, A., Lo, M., Marques, T. G., Mariconti, E., & Pasculli, L. (2026) · Journal of Cybersecurity · doi:10.1093/cybsec/tyag016

Does the Community Understand the Community Guidelines?

Quint, F., Theocharis, Y., Nahrgang, M., Weidmann, N. B., & Roberts, M. E. (2026) · Policy & Internet · doi:10.1002/poi3.70045

AnBridge: Protecting On-Device AI with Android Virtualization Framework

Farina, G., Corte, R. D., Machiry, A., Cinque, M., & Bagchi, S. (2026) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn69566.2026.00081

Legal Ambiguity Meets Artificial Intelligence: Unfair Trade Practices Under Indian Law and the Challenge of Automated Interpretation

Chauhan, K. D. S., & Anupriya (2026) · Springer eBooks · doi:10.1007/978-3-031-87993-7_190-1

COMPASS: The 2nd International Workshop on Socio-technical Approaches to Content Moderation and Platform Governance

Caplan, R., Cresci, S., Goanta, C., Jhaver, S., Kairam, S. R., Saha, K., & Zannettou, S. (2026) · Proceedings of the 2026 Companion of the Computer-Supported Cooperative Work and Social Computing · doi:10.1145/3785651.3818816

Characterizing Community Guidelines on Social Media Platforms

Jiang, J. A., Middler, S., Brubaker, J. R., & Fiesler, C. L. (2020) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3406865.3418312 · Cited by 37

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Rogers, R. (2026) · Routledge eBooks · doi:10.4324/9781003693192-1

A comparative analysis of social networks content moderation guidelines and blog posts

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TORA: Topic-based rule affinity for content moderation in social media communities

Wang, K., Fu, Z., Xin, W., Hanson, K., & Zhou, L. (2026) · Decision Support Systems · doi:10.1016/j.dss.2026.114750 · Cited by 1

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Rogers, R. (2026) · Routledge eBooks · doi:10.4324/9781003693192