Vocabulary-based Community Detection and Characterization

被引:7
作者
Ramponi, Giorgia [1 ]
Brambilla, Marco [1 ]
Ceri, Stefano [1 ]
Daniel, Florian [1 ]
Di Giovanni, Marco [1 ]
机构
[1] Politecn Milan, Milan, Italy
来源
SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING | 2019年
关键词
Social analytics; community detection; content-based data analytics;
D O I
10.1145/3297280.3297384
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
With the increase of digital interaction, social networks are becoming an essential ingredient of our life, by progressively becoming the dominant media, e.g. in influencing political choices. 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 argue that the vocabulary of terms used in social interaction is a very distinctive feature of a community, hence it can be effectively used for community detection. We show that, by inspecting the vocabulary used by tweets, we can achieve very efficient classifiers and predictors of account membership within a given community. We describe the syntactic and semantic features that best constitute a vocabulary, then we provide their comparative evaluation and select the best features for the task, and finally we illustrate several applications of our approach to concrete community detection scenarios.
引用
收藏
页码:1043 / 1050
页数:8
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