同时建模在线社区的结构与语言演化，揭示极端群体行为模式。
Jointly modelling the evolution of social structure and language in online communities
- 联合建模用户社交结构与语言随时间演变
- 在聚类和成员预测任务中优于传统静态模型
- 可追踪事件响应与暴力语言倾向，适合安全研究
群体互动发生在特定的社会时间背景中，建模在线社区交互时应考虑这一因素。本文提出一种联合建模社区结构与语言随时间演化的方法，生成动态的词向量与用户表示，可用于用户聚类、分析群体主题兴趣及预测群组归属。我们在一组厌女极端主义群体上应用并评估该方法，结果表明，相比仅关注社交结构或使用静态词嵌入的先前模型，本方法在聚类与嵌入预测任务中表现更优。此外，该方法支持对在线群体的新类型分析，如追踪其对时间事件的响应以及量化其使用暴力语言的倾向，这对极端主义群体研究具有重要意义。
原文摘要 · Abstract (English)
Group interactions take place within a particular socio-temporal context, which should be taken into account when modelling interactions in online communities. We propose a method for jointly modelling community structure and language over time. Our system produces dynamic word and user representations that can be used to cluster users, investigate thematic interests of groups, and predict group membership. We apply and evaluate our method in the context of a set of misogynistic extremist groups. Our results indicate that this approach outperforms prior models which lacked one of these components (i.e. not incorporating social structure, or using static word embeddings) when evaluated on clustering and embedding prediction tasks. Our method further enables novel types of analyses on online groups, including tracing their response to temporal events and quantifying their propensity for using violent language, which is of particular importance in the context of extremist groups.
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