Analysing how people react to rumours associated with news in social media is an important task to prevent the spreading of misinformation, which is nowadays widely recognized as a dangerous tendency. In social media conversations, users show different stances and attitudes towards rumourous stories. Some users take a definite stance, supporting or denying the rumour at issue, while others just comment it, or ask for additional evidence on the rumour’s veracity. A shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets. The goal is predicting user stance towards emerging rumours in Twitter, in terms of supporting, denying, querying, or commenting the original rumour, looking at the conversation threads originated by the rumour. This paper describes a new approach to this task, where the use of conversation-based and affective-based features, covering different facets of affect, is explored. Our classification model outperforms the best-performing systems for stance classification at SemEval-2017 showing the effectiveness of the feature set proposed.

Stance classification for rumour analysis in Twitter: Exploiting affective information and conversation structure

Pamungkas E. W.;Basile V.;Patti V.
2019-01-01

Abstract

Analysing how people react to rumours associated with news in social media is an important task to prevent the spreading of misinformation, which is nowadays widely recognized as a dangerous tendency. In social media conversations, users show different stances and attitudes towards rumourous stories. Some users take a definite stance, supporting or denying the rumour at issue, while others just comment it, or ask for additional evidence on the rumour’s veracity. A shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets. The goal is predicting user stance towards emerging rumours in Twitter, in terms of supporting, denying, querying, or commenting the original rumour, looking at the conversation threads originated by the rumour. This paper describes a new approach to this task, where the use of conversation-based and affective-based features, covering different facets of affect, is explored. Our classification model outperforms the best-performing systems for stance classification at SemEval-2017 showing the effectiveness of the feature set proposed.
2019
2nd International Workshop on Rumours and Deception in Social Media (RDSM 2018)
Torino, Italy, October 22, 2018.
2018
Proceedings of the CIKM 2018 Workshops co-located with 27th ACM International Conference on Information and Knowledge Management (CIKM 2018)
CEUR-WS
2482
1
7
http://ceur-ws.org/Vol-2482/paper37.pdf
Stance Classification, Rumor Analysis, Twitter, Misinformation
Pamungkas E.W.; Basile V.; Patti V.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1721736
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