Affiliations: [a] Dipartimento di Informatica, Università degli Studi di Torino, C.so Svizzera 185, Turin, 10149, Italy
| [b] PRHLT Research Center, Universitat Politècnica de València, Camino de Vera s/n. Valencia, 46022, Spain
Correspondence:
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Corresponding author. Mirko Lai, Dipartimento di Informatica, Università degli Studi di Torino, C.so Svizzera 185, 10149, Turin, Italy. E-mail: lai@di.unito.it.
Abstract: Interest has grown around the classification of stance that users assume within online debates in recent years. Stance has been usually addressed by considering users posts in isolation, while social studies highlight that social communities may contribute to influence users’ opinion. Furthermore, stance should be studied in a diachronic perspective, since it could help to shed light on users’ opinion shift dynamics that can be recorded during the debate. We analyzed the political discussion in UK about the BREXIT referendum on Twitter, proposing a novel approach and annotation schema for stance detection, with the main aim of investigating the role of features related to social network community and diachronic stance evolution. Classification experiments show that such features provide very useful clues for detecting stance.
Keywords: Stance detection, Twitter, brexit, NLP, community detection
