Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions
arXiv:1702.01119 · doi:10.1145/2998181.2998213
Abstract
In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.
arXiv commentBest Paper Award at CSCW 2017
References in corpus (3)
Cited by in corpus (48)
- Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
- Fake News Detection on Social Media: A Data Mining Perspective
- Community Interaction and Conflict on the Web
- Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incels
- The Structure of Toxic Conversations on Twitter
- A Trade-off-centered Framework of Content Moderation
- How we do things with words: Analyzing text as social and cultural data
- Conversations Gone Awry: Detecting Early Signs of Conversational Failure
- Conspiracy vs science: A large-scale analysis of online discussion cascades
- Modeling Interdependent and Periodic Real-World Action Sequences
- The few-get-richer: a surprising consequence of popularity-based rankings
- Ten Social Dimensions of Conversations and Relationships
- Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations
- Interpretable Multi-Modal Hate Speech Detection
- Opinion Conflicts: An Effective Route to Detect Incivility in Twitter
- Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
- Thread With Caution: Proactively Helping Users Assess and Deescalate Tension in Their Online Discussions
- Learning Dynamic Embeddings from Temporal Interactions
- What Makes a Good Team? A Large-scale Study on the Effect of Team Composition in Honor of Kings
- Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites
- Intergroup Contact in the Wild: Characterizing Language Differences between Intergroup and Single-group Members in NBA-related Discussion Forums
- "You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks
- Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media
- Characterizing Twitter Users Who Engage in Adversarial Interactions against Political Candidates
- Critical Thinking for Language Models
- A Critical Reflection on the Use of Toxicity Detection Algorithms in Proactive Content Moderation Systems
- One-Class Adversarial Nets for Fraud Detection
- Cura: Curation at Social Media Scale
- Cheating in online gaming spreads through observation and victimization
- Countering Online Hate Speech: An NLP Perspective
- AI Rules? Characterizing Reddit Community Policies Towards AI-Generated Content
- On the Social and Technical Challenges of Web Search Autosuggestion Moderation
- TrollSpot: Detecting misbehavior in commenting platforms
- Persuasion or Insulting? Unpacking Discursive Strategies of Gender Debate in Everyday Feminism in China
- Analyzing Social Media Activities at Bellingcat
- The influence of coordinated behavior on toxicity
- Learning from Fact-checkers: Analysis and Generation of Fact-checking Language
- Mitigating the Backfire Effect Using Pacing and Leading
- Consequential Ranking Algorithms and Long-term Welfare
- "There Has To Be a Lot That We're Missing": Moderating AI-Generated Content on Reddit
- Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop
- Toxic comments reduce the activity of volunteer editors on Wikipedia
- Unified Game Moderation: Soft-Prompting and LLM-Assisted Label Transfer for Resource-Efficient Toxicity Detection
- Echoes of Norms: Investigating Counterspeech Bots' Influence on Bystanders in Online Communities
- More Specificity, More Attention to Social Context: Reframing How We Address "Bad Actors"
- Understanding Toxic Interaction Across User and Video Clusters in Social Video Platforms
- In Bad Faith: Assessing Discussion Quality on Social Media
- AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Media