Sequential Approach to Rumour Stance Classification


Rumour stance classification is a task that involves identifying the attitude of Twitter users towards the truthfulness of the rumour they are discussing. Stance classification is considered to be an important step towards rumour verification, therefore performing well in this task is expected to be useful in debunking false rumours. In this work we classify a set of Twitter posts discussing rumours into either supporting, denying, questioning or commenting on the underlying rumours. We propose an LSTM-based sequential model that, through modelling the conversational structure of tweets, obtains state-of-theart accuracy on the SemEval-2017 RumourEval dataset.

Proceedings of the First ACL Workshop on Women and Underrepresented Minorities in Natural Language Processing (WiNLP) at the 55th Annual Meeting of the Association for Computational Linguistics (ACL)