Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure
This work addresses the challenge of misinformation spread in social media by improving stance classification for rumour analysis, though it is incremental as it builds on existing shared task benchmarks.
The paper tackled the problem of classifying user stances towards rumours on Twitter, using conversation-based and affective features, and achieved state-of-the-art performance by outperforming the best systems in the SemEval-2017 shared task.
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 related to the veracity of the rumour. On this line, a new 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, has been explored. Our classification model outperforms the best-performing systems for stance classification at SemEval-2017 Task 8, showing the effectiveness of the feature set proposed.