[论文解读] Cheaters in the Steam Community Gaming Social Network

Jeremy Blackburn, Ramanuja Simha+6 more|arXiv (Cornell University)|Dec 21, 2011

Digital Games and Media参考文献 27被引用 3

一句话总结

本研究利用来自超过1200万名用户的Steam社区游戏社交网络数据以及来自10,000名玩家的游戏内互动数据，分析了作弊行为。研究发现，作弊者在社交网络中具有良好的社会嵌入性，作弊行为通过朋友影响在社交圈内传播，且尽管仍活跃于社区，作弊者仍面临诸如好友流失和隐私设置提高等社会惩罚。

ABSTRACT

Online gaming is a multi-billion dollar industry that entertains a large, global population. One unfortunate phenomenon, however, poisons the competition and the fun: cheating. The costs of cheating span from industry-supported expenditures to detect and limit cheating, to victims' monetary losses due to cyber crime. This paper studies cheaters in the Steam Community, an online social network built on top of the world's dominant digital game delivery platform. We collected information about more than 12 million gamers connected in a global social network, of which more than 700 thousand have their profiles flagged as cheaters. We also collected in-game interaction data of over 10 thousand players from a popular multiplayer gaming server. We show that cheaters are well embedded in the social and interaction networks: their network position is largely undistinguishable from that of fair players. We observe that the cheating behavior appears to spread through a social mechanism: the presence and the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future. Also, we observe that there is a social penalty involved with being labeled as a cheater: cheaters are likely to switch to more restrictive privacy settings once they are tagged and they lose more friends than fair players. Finally, we observe that the number of cheaters is not correlated with the geographical, real-world population density, or with the local popularity of the Steam Community. This analysis can ultimately inform the design of mechanisms to deal with anti-social behavior (e.g., spamming, automated collection of data) in generic online social networks.

研究动机与目标

• 理解作弊者在Steam社区游戏平台中的社交网络位置。
• 调查作弊行为是否通过玩家之间的社交影响而传播。
• 分析被公开标记为作弊者后的社会后果，包括隐私和友谊关系的变化。
• 检验地理或人口密度因素是否与作弊行为的普遍性相关。
• 为反作弊机制及在线社交网络中更广泛的反社会行为检测提供依据。

提出的方法

• 通过网页爬取收集超过1200万名Steam用户的资料，包括社交关系和VAC封禁状态。
• 从一个受欢迎的多人游戏服务器收集超过10,000名玩家的游戏内互动日志，以分析行为模式。
• 使用Amazon Elastic MapReduce上的MapReduce处理大规模网络指标，包括度中心性与介数中心性。
• 应用地理社会度量以评估作弊行为的空间聚类与区域模式。
• 训练机器学习模型（逻辑回归、朴素贝叶斯、决策树）基于资料与网络特征对玩家是否为作弊者进行分类。
• 进行时间序列分析，评估玩家的作弊朋友数量与其未来成为作弊者的可能性之间的相关性。

实验结果

研究问题

• RQ1 作弊者在Steam社区的社交与互动网络中处于何种位置？
• RQ2 作弊朋友的存在及其数量是否能预测普通玩家未来成为作弊者的可能性？
• RQ3 被公开标记为作弊者后，玩家会面临哪些社会后果？
• RQ4 作弊者的分布是否与现实世界中的地理人口密度或本地Steam受欢迎程度相关？
• RQ5 基于网络的特征能否提升在线游戏环境中对作弊者的检测能力？

主要发现

• 作弊者在社交网络中嵌入良好，其在网络中心性指标上的表现与公平玩家基本无法区分。
• 作弊朋友的存在及其数量显著影响普通玩家未来成为作弊者的可能性，表明作弊行为具有社交传播性。
• 被标记后，作弊者更可能将个人资料设为私密，暗示其可能感到社会尴尬或不适。
• 与公平玩家相比，作弊者随时间推移面临更大的好友流失，表明存在可量化的社会惩罚。
• 作弊行为的普遍性与地理人口密度或本地Steam受欢迎程度无显著相关性，表明文化或社区层面因素可能驱动作弊行为。
• 利用资料与网络特征的机器学习模型可实现65%至74%的作弊者分类准确率。