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Content-based characterization of online social communities
Information Processing & Management ( IF 8.6 ) Pub Date : 2019-10-15 , DOI: 10.1016/j.ipm.2019.102133
Giorgia Ramponi , Marco Brambilla , Stefano Ceri , Florian Daniel , Marco Di Giovanni

Nowadays social networks are becoming an essential ingredient of our life, the faster way to share ideas and to influence people. Interaction within social networks tends to take place within communities, sets of social accounts which share friendships, ideas, interests and passions; detecting digital communities is of increasing relevance, from a social and economical point of view.

In this paper, we analyze the problem of community detection from a content analysis perspective: we argue that the content produced in social interaction is a very distinctive feature of a community, hence it can be effectively used for community detection. We analyze the problem from a textual perspective using only syntactic and semantic features, including high level latent features that we denote as topics.

We show that, by inspecting the content used by tweets, we can achieve very efficient classifiers and predictors of account membership within a given community. We describe the features that best constitute a vocabulary, then we provide their comparative evaluation and select the best features for the task, and finally we illustrate an application of our approach to some concrete community detection scenarios, such as Italian politics and targeted advertising.



中文翻译:

基于内容的在线社交社区表征

如今,社交网络已成为我们生活中不可或缺的组成部分,是分享想法和影响人们的更快方法。社交网络之间的互动往往发生在社区内,这些社交账户集共享友谊,想法,兴趣和激情。从社会和经济的角度来看,检测数字社区的重要性与日俱增。

在本文中,我们从内容分析的角度分析了社区检测的问题:我们认为,社交互动中产生的内容是社区的一个非常独特的特征,因此可以有效地用于社区检测。我们仅使用语法和语义功能(包括我们称为主题的高级潜在功能)从文本角度分析问题。

我们证明,通过检查推文使用的内容,我们可以在给定社区中实现非常有效的帐户成员身份分类和预测。我们描述了最能构成词汇的特征,然后提供了它们的比较评估,并为任务选择了最佳特征,最后我们说明了我们的方法在某些具体的社区侦查场景中的应用,例如意大利政治和定向广告。

更新日期:2020-04-21
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