识别归属：使用行为痕迹来预测网络游戏中社交互动的质量
Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games
摘要：
多人游戏中的在线社交互动可能是支持性的、积极的，也可能是有害的、有害的;然而，很少有方法可以轻松评估游戏中的人际互动质量。我们使用行为痕迹来预测二元陌生人之间的联系，通过他们在网络游戏环境中的社交互动来促进。我们从23个二人组中收集了音频、视频、游戏中和自我报告数据，提取了75个特征，训练了随机森林和支持向量机模型，并评估了它们的性能预测二进制（高/低）以及对合作伙伴的持续从属关系。这些模型可以预测二元和连续的联系，在看不见的数据上的准确率（F1）高达79.1%，解释方差（R2）高达20.1%，其中基于口头交流的特征表现出最高的潜力。我们的研究结果可以为多人游戏和游戏社区的设计提供信息，并指导在线游戏中匹配和减轻有毒行为的系统的开发。
英文摘要：
Online social interactions in multiplayer games can be supportive and positive or toxic and harmful; however, few methods can easily assess interpersonal interaction quality in games. We use behavioural traces to predict affiliation between dyadic strangers, facilitated through their social interactions in an online gaming setting. We collected audio, video, in-game, and self-report data from 23 dyads, extracted 75 features, trained Random Forest and Support Vector Machine models, and evaluated their performance predicting binary (high/low) as well as continuous affiliation toward a partner. The models can predict both binary and continuous affiliation with up to 79.1% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with features based on verbal communication demonstrating the highest potential. Our findings can inform the design of multiplayer games and game communities, and guide the development of systems for matchmaking and mitigating toxic behaviour in online games.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
