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Long Chains or Stable Communities? The Role of Emotional Stability in Twitter Conversations
Author(s) -
Celli Fabio,
Rossi Luca
Publication year - 2015
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12023
Subject(s) - neuroticism , big five personality traits , focus (optics) , computer science , stability (learning theory) , personality , cluster analysis , exploit , symbol (formal) , set (abstract data type) , social media , punctuation , psychology , world wide web , social psychology , artificial intelligence , machine learning , computer security , physics , optics , programming language
In this article, we address the issue of how emotional stability affects social relationships in Twitter. In particular, we focus our study on users’ communicative interactions, identified by the symbol “@.” We collected a corpus of about 200,000 Twitter posts, and we annotated it with our personality recognition system. This system exploits linguistic features, such as punctuation and emoticons, and statistical features, such as follower count and retweeted posts. We tested the system on a data set annotated with personality models produced by human subjects and against a software for the analysis of Twitter data. Social network analysis shows that, whereas secure users have more mutual connections, neurotic users post more than secure ones and have the tendency to build longer chains of interacting users. Clustering coefficient analysis reveals that, whereas secure users tend to build stronger networks, neurotic users have difficulty in belonging to a stable community; hence, they seek for new contacts in online social networks.