Predicting Social Exclusion: A Study of Linguistic Ostracism in Social Networks

Greta Gandolfi, C. Strapparava
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Abstract

Ostracism is a community-level phenomenon, shared by most social animals, including humans. Its detection plays a crucial role for the individual, with possible evolutionary consequences for the species. Considering (1) its bound with communication and (2) its social nature, we hypothesise the combination of (a) linguistic and (b) community-level features to have a positive impact on the automatic recognition of ostracism in human online communities. We model an English linguistic community through Reddit data and we analyse the performance of simple classification algorithms. We show how models based on the combination of (a) and (b) generally outperform the same architectures when fed by (a) or (b) in isolation.1
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预测社会排斥:社会网络中的语言排斥研究
排斥是一种社区层面的现象,大多数群居动物,包括人类,都有这种现象。它的检测对个体起着至关重要的作用,可能对物种的进化产生影响。考虑到(1)它与交流的联系和(2)它的社会性质,我们假设(a)语言和(b)社区层面特征的结合对人类在线社区中排斥的自动识别产生了积极的影响。我们通过Reddit数据建立了一个英语语言社区模型,并分析了简单分类算法的性能。我们展示了当(a)或(b)单独提供时,基于(a)和(b)组合的模型通常如何优于相同的架构
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