Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games

By Haewoon Kwak, Jeremy Blackburn, Seungyeop Han. 2015

279 citations recorded by the source corpus.

In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.

Subfields: Artificial Intelligence, Social Psychology, Sociology and Political Science, Natural Language Processing, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Diyi Yang
2. Lucas Dixon
3. Nithum Thain
4. Jiaao Chen
5. Jisun An
6. Fan Huang
7. Ashwin Rajadesingan
8. Paul Resnick

In Social Psychology

1. Cristian Danescu-Niculescu-Mizil
2. Paul Resnick
3. Diyi Yang
4. Lucas Dixon
5. Ashwin Rajadesingan
6. Nithum Thain

In Sociology and Political Science

1. Cristian Danescu-Niculescu-Mizil
2. Caleb Ziems
3. Paul Resnick
4. Ceren Budak
5. Diyi Yang
6. Ashwin Rajadesingan
7. Yong-Yeol Ahn
8. Pan Hui

In Natural Language Processing

1. Diyi Yang
2. Jiaao Chen
3. Jisun An
4. Fan Huang
5. Haewoon Kwak
6. Omar Shaikh
7. Cristian Danescu-Niculescu-Mizil
8. Caleb Ziems

In Safety, Robustness & Interpretability

1. Lucas Dixon
2. Paul Resnick
3. Ceren Budak
4. Ashwin Rajadesingan
5. Nithum Thain
6. Diyi Yang
7. Jisun An
8. Omar Shaikh

Citations in the corpus

• Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech (2023, background): “Considering the diversity and nuanced nature of the implicit hate speech [7] and overall toxic behavior online [14], it is not surprising that lay people’s decisions can be affected by additional context.”
• All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, background): “For instance, the users’ misbehaviour can be vague and difficult to identify [679].”
• Conversations Gone Awry: Detecting Early Signs of Conversational Failure (2018, background): “…examining behaviors like aggression (Kayany, 1998), harassment (Chatzakou et al., 2017; Vitak et al., 2017), and bullying (Akbulut et al., 2010; Kwak et al., 2015; Singh et al., 2017), as well as their impact on aspects of engagement like user retention (Collier and Bear, 2012; Wikimedia…”
• Can Large Language Models Transform Computational Social Science? (2023, background): “, 2020) and the effectiveness of intervention methods (Kwak et al., 2015).”
• Predicting Anti-Asian Hateful Users on Twitter during COVID-19 (2021, background): “, 2018), online games (Kwak et al., 2015), and many other online platforms (Zannettou et al.”
• Quick, Community-Specific Learning: How Distinctive Toxicity Norms Are Maintained in Political Subreddits (2020, background): “Research on League of Legends, an online multiplayer game, showed that toxic players exhibit behavior similar to others at the start of a match but change their behavior during the match (Kwak, Blackburn, and Han 2015).”