论文标题
认识隶属关系：使用行为痕迹预测在线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
论文作者
论文摘要
多人游戏中的在线社交互动可以是支持，积极或有毒和有害的；但是，很少有方法可以轻松地评估游戏中人际关系的互动质量。我们使用行为痕迹来预测二元陌生人之间的隶属关系，这是通过在线游戏环境中的社交互动来促进的。我们从23个二元组收集了音频，视频，游戏中和自我报告数据，提取了75个功能，训练有素的随机森林和支持向量机模型，并评估了他们预测二进制（高/低）的性能以及与伙伴的连续隶属关系。这些模型可以预测高达79.1％的准确性（F1）和20.1％的二进制和连续隶属关系，在看不见的数据上解释了方差（R2），其特征基于口头交流，证明了最高潜力。我们的发现可以为多人游戏和游戏社区的设计提供信息，并指导开发在线游戏中对媒体和减轻有毒行为的系统的开发。
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.
