A sifted multi-task learning model with gated and attention-based shared feature selection reports state-of-the-art F1 on RumourEval and PHEME for fake news detection.
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
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abstract
This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour stance classification, which 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 a LSTM-based sequential model that, through modelling the conversational structure of tweets, which achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A.
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Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection
A sifted multi-task learning model with gated and attention-based shared feature selection reports state-of-the-art F1 on RumourEval and PHEME for fake news detection.