Graduation Year

2014

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Department

Computer Engineering

Degree Granting Department

Computer Science and Engineering

Major Professor

Adriana Iamnitchi, Ph.D.

Co-Major Professor

John Skvoretz, Ph.D.

Committee Member

John Skvoretz, Ph.D.

Committee Member

Cristian Borcea, Ph.D.

Committee Member

Jay Ligatti, Ph.D.

Committee Member

Ken Christensen, Ph.D.

Committee Member

Sanjukta Bhanja, Ph.D.

Keywords

big data, cheating, contagion, social network analysis, toxic behavior

Abstract

This dissertation studies bad behavior at large-scale using data traces from online video games. Video games provide a natural laboratory for exploring bad behavior due to their popularity, explicitly defined (programmed) rules, and a competitive nature that provides motivation for bad behavior. More specifically, we look at two forms of bad behavior: cheating and toxic behavior.

Cheating is most simply defined as breaking the rules of the game to give one player an edge over another. In video games, cheating is most often accomplished using programs, or "hacks," that circumvent the rules implemented by game code. Cheating is a threat to the gaming industry in that it diminishes the enjoyment of fair players, siphons off money that is paid to cheat creators, and requires investment in anti-cheat technologies.

Toxic behavior is a more nebulously defined term, but can be thought of as actions that violate social norms, especially those that harm other members of the society. Toxic behavior ranges from insults or harassment of players (which has clear parallels to the real world) to domain specific instances such as repeatedly "suiciding"" to help an enemy team. While toxic behavior has clear parallels to bad behavior in other online domains, e.g., cyberbullying, if gone unchecked it has the potential to "kill" a game by driving away its players.

We first present a distributed architecture and reference implementation for the collection and analysis of large-scale social data. Using this implementation we then study the social structure of over 10 million gamers collected from a planetary scale Online Social Network, about 720 thousand of whom have been labeled cheaters, finding a significant correlation between social structure and the probability of partaking in cheating behavior. We additionally collect over half a billion daily observations of the cheating status of these gamers. Using about 10 months of detailed server logs from a community owned and operated game server we next analyze how relationships in the aforementioned online social network are backed by in-game interactions. Next, we use the insights gained and find evidence for a contagion process underlying the spread of cheating behavior and perform a data driven simulation using mathematical models for contagion. Finally, we build a model using millions of crowdsourced decisions for predicting toxic behavior in online games.

To the best of our knowledge, this dissertation presents the largest study of bad behavior to date. Our findings confirm theories about cheating and unethical behavior that have previously remained untested outside of controlled laboratory experiments or only with small, survey based studies. We find that the intensity of interactions between players is a predictor of a future relationship forming. We provide statistically significant evidence for cheating as a contagion. Finally, we extract insights from our model for detecting toxic behavior on how human reviewers perceive the presence and severity of bad behavior.

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