This paper focuses on the role of social relations within social media in the formation of public opinion. We propose to combine the detection of the users’ stance towards BREXIT, carried out by content analysis of Twitter messages, and the exploration of their social relations, by relying on social network analysis. The analysis of a novel Twitter corpus on the BREXIT debate, developed for our purposes, shows that like-minded individuals (sharing the same opinion towards the specific issue) are likely belonging to the same social network community. Moreover, opinion driven homophily is exhibited among neighbours. Interestingly, users’ stance shows diachronic evolution.

Extracting Graph Topological Information and Users’ Opinion

Mirko Lai;Marcella Tambuscio;Viviana Patti;Giancarlo Ruffo;
2017-01-01

Abstract

This paper focuses on the role of social relations within social media in the formation of public opinion. We propose to combine the detection of the users’ stance towards BREXIT, carried out by content analysis of Twitter messages, and the exploration of their social relations, by relying on social network analysis. The analysis of a novel Twitter corpus on the BREXIT debate, developed for our purposes, shows that like-minded individuals (sharing the same opinion towards the specific issue) are likely belonging to the same social network community. Moreover, opinion driven homophily is exhibited among neighbours. Interestingly, users’ stance shows diachronic evolution.
2017
International Conference of the Cross-Language Evaluation Forum for European Languages
Dublin, Ireland
11-14 September
Lecture Notes in Computer Science
Springer
10456
112
118
978-3-319-65812-4
978-3-319-65813-1
https://link.springer.com/chapter/10.1007/978-3-319-65813-1_10
https://www.researchgate.net/publication/317638198_Extracting_Graph_Topological_Information_and_Users%27_Opinion
Stance detection, BREXIT, Community detection
Mirko Lai, Marcella Tambuscio, Viviana Patti, Giancarlo Ruffo, Paolo Rosso
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1652430
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