SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
arXiv:2206.14855 · doi:10.1109/EuroSP57164.2023.00056
Abstract
Social media platforms have been establishing content moderation guidelines and employing various moderation policies to counter hate speech and misinformation. The goal of this paper is to study these community guidelines and moderation practices, as well as the relevant research publications, to identify the research gaps, differences in moderation techniques, and challenges that should be tackled by the social media platforms and the research community. To this end, we study and analyze fourteen most popular social media content moderation guidelines and practices, and consolidate them. We then introduce three taxonomies drawn from this analysis as well as covering over two hundred interdisciplinary research papers about moderation strategies. We identify the differences between the content moderation employed in mainstream and fringe social media platforms. Finally, we have in-depth applied discussions on both research and practical challenges and solutions.
To appear in the 8th IEEE European Symposium on Security and Privacy (EuroS&P 2023)
References in corpus (19)
- Deep Learning for Hate Speech Detection in Tweets
- Towards Understanding and Detecting Fake Reviews in App Stores
- Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations
- An Online Multilingual Hate speech Recognition System
- Combating Misinformation in Bangladesh: Roles and Responsibilities as Perceived by Journalists, Fact-checkers, and Users
- NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges
- Comparing the Perceived Legitimacy of Content Moderation Processes: Contractors, Algorithms, Expert Panels, and Digital Juries
- Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge
- Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald
- Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation
- Hateminers : Detecting Hate speech against Women
- Interpretable Multi-Modal Hate Speech Detection
- Finding Strategies Against Misinformation in Social Media: A Qualitative Study
- Constraint 2021: Machine Learning Models for COVID-19 Fake News Detection Shared Task
- Social Media COVID-19 Misinformation Interventions Viewed Positively, But Have Limited Impact
- Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities
- Cybersecurity Misinformation Detection on Social Media: Case Studies on Phishing Reports and Zoom's Threats
- Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification
- Feels Bad Man: Dissecting Automated Hateful Meme Detection Through the Lens of Facebook's Challenge
Cited by in corpus (9)
- "Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies
- Cybersecurity Misinformation Detection on Social Media: Case Studies on Phishing Reports and Zoom's Threats
- One of Many: Assessing User-level Effects of Moderation Interventions on r/The_Donald
- Verifying the Robustness of Automatic Credibility Assessment
- How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape
- Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policies
- Beyond Trial-and-Error: Predicting User Abandonment After a Moderation Intervention
- On mission Twitter Profiles: A Study of Selective Toxic Behavior
- Exploring the Distinctive Tweeting Patterns of Toxic Twitter Users