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Deviant behavior in League of Legends: Do jerks drive off other players?

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This post describes work appearing at CSCW 2014.

Many online activities today involve interacting with other people, and these interactions are often dictated by social norms – unspoken rules that classify socially acceptable behavior.  Whether intentional or otherwise, people sometimes break these rules.  Behavior that goes against established norms is called deviant behavior, and in many cases is assumed to be negative. In online communities, deviant behavior is commonly believed to be harmful, and is expected to drive users to leave the community. For example, intentionally incorrect responses on a question and answer site may discourage new users from asking questions. Our work looks at patterns of player behavior in an online game where people will play bingo for money. We build a metric to predict a specific kind of deviant behavior, toxicity, and we use this metric to examine whether deviant behavior causes other players to quit.

We look at deviant behavior in the popular online game League of Legends.  League of Legends is a competitive multiplayer game where two teams of five players compete to destroy the other team’s base. Each player controls a single character, and every character has unique abilities which, when used skillfully, can help the player’s team overcome an enemy team. If you’ve never played League of Legends, check out this four-minute introduction video from League’s developer, Riot Games.

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GroupLens has gathered for a photograph

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Look at this group of nice folks! This GroupLens group photo was taken in the atrium of Keller Hall, where we work.

grouplens-2013

GroupLens, Fall 2013, back to front:

  • Jacob, Raghav, Zihong, Kate
  • Yilin, Brent, Morten
  • Pik-Mai, Steven, Dan
  • Zihong, Derian, Fernando
  • Vlad, Anu, Michael
  • Ting, Alison, Loren
  • Vikas, Max, Joe
  • Andrew, Daniel, Tien

Technology Review on Wikipedia’s decline

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wikipedia decline

The number of active editors is plotted over time for the English Language Wikipedia

 

Tom Simonite at Technology Review just published a great piece covering “The Decline of Wikipedia” where they cite my my work (published in American Behavioral Scientist, see also the free preprint) with Geiger, Morgan and Riedl exploring potential reasons for Wikipedia’s declining pool of editors (see figure above).  In that work, we manually categorized newcomers to Wikipedia by the quality of their edits and built a set of models to predict which high quality newcomers would continue editing and which ones would leave the project.  We showed that the reason for the decline is not due to the the quality of newcomers but rather the reception they receive; newcomers whose work is immediately rejected and who are sent warning messages about their behavior don’t come back.  It looks like the dramatic change in 2007 corresponds to the introduction of counter-vandalism robots and automated tools in Wikipedia that were used to reject newcomers’ edits.

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Similarity Functions for User-User Collaborative Filtering

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Typically, user-user collaborative filtering has used Pearson correlation to compare users. Early work tried Spearman correlation and (raw) cosine similarity, but found Pearson to work better, and the issue wasn’t revisited for quite some time.

When I was revisiting some of these algorithmic decisions for the LensKit paper, I tried cosine similarity on mean-centered vectors (sometimes called ‘Adjusted Cosine’) and found it to work better (on our offline evaluation metrics) than Pearson correlation, even without any significance weighting. So now my recommendation is to use cosine similarity over mean-centered data. But why the change, and why does it work?

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GroupLens to have five papers at CSCW 2014

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We’re happy to announce that GroupLens had five papers accepted at ACM CSCW 2014, a high-profile social computing conference:

  • “Specialization, Homophily, and Gender in a Social Curation Site: Findings from Pinterest” – Shuo Chang (GroupLens), Vikas Kumar (GroupLens), Eric Gilbert (Georgia Tech), Loren Terveen (GroupLens)
  • “Managing Political Differences in Social Media” – Catherine Grevet (Georgia Tech), Loren Terveen (GroupLens), Eric Gilbert (Georgia Tech)
  • “Leveraging the Contributory Potential of User Feedback” – Mikhil Masli (GroupLens), Loren Terveen (GroupLens)
  • “Capturing Quality: Retaining Provenance for Curated Volunteer Monitoring Data” – S. Andrew Sheppard (GroupLens), Andrea Wiggins (Cornell University), Loren Terveen (GroupLens)
  • “To Search or to Ask: The Routing of Information Needs Between Traditional Search Engines and Social Networks” – Anne Oeldorf-Hirsch (Northwestern University), Brent Hecht (GroupLens), Merrie Morris (Microsoft Research), Jaime Teevan (Microsoft Research), Darren Gergle (Northwestern University)

Special thanks to our collaborators at Georgia Tech’s comp.social lab, Northwestern’s CollabLab, DataONE, and Microsoft Research. Stay tuned for preprints and blog posts on each paper!