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【6h】Using Sentiment Induction to Understand Variation in Gendered Online Communities

机译使用情绪归纳法了解性别在线社区中的变化

【摘要】We analyze gendered communities defined in three different ways: text, users, and sentiment. Differences across these representations reveal facets of communities' distinctive identities, such as social group, topic, and attitudes. Two communities may have high text similarity but not user similarity or vice versa, and word usage also does not vary according to a clearcut, binary perspective of gender. Community-specific sentiment lexicons demonstrate that sentiment can be a useful indicator of words' social meaning and community values, especially in the context of discussion content and user demographics. Our results show that social platforms such as Reddit are active settings for different constructions of gender.

【摘要机译】我们分析了以三种不同方式定义的性别社区:文本,用户和情感。这些表示形式之间的差异揭示了社区独特身份的各个方面,例如社会群体,主题和态度。两个社区可能具有高度的文本相似性,但没有用户相似性,反之亦然,并且单词用法也不会因性别的明晰二元视角而有所不同。特定于社区的情感词典表明,情感可以作为词语的社会意义和社区价值的有用指示,尤其是在讨论内容和用户人口统计的情况下。我们的结果表明,Reddit等社交平台是针对不同性别建构的活跃环境。

【作者】Li Lucy;Julia Mendelsohn;

【作者单位】Symbolic Systems Program Department of Computer Science Stanford University; Department of Linguistics Department of Computer Science Stanford University;

【年(卷),期】2019(),

【年度】2019

【页码】156-166

【总页数】11

【原文格式】PDF

【正文语种】eng

【中图分类】;

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