Long Chains or Stable Communities? The Role of Emotional Stability in Twitter Conversations
Article first published online: 4 NOV 2013
© 2013 Wiley Periodicals, Inc.
Volume 31, Issue 1, pages 184–200, February 2015
How to Cite
2015), Long Chains or Stable Communities? The Role of Emotional Stability in Twitter Conversations, Computational Intelligence, 31, 184–200, doi: 10.1111/coin.12023, and (
- Issue published online: 10 FEB 2015
- Article first published online: 4 NOV 2013
- Manuscript Accepted: 10 MAR 2013
- Manuscript Revised: 17 DEC 2012
- Manuscript Received: 17 JUL 2012
- data mining;
- personality recognition;
- social network analysis;
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.