A temporal attentional model for rumor stance classification

APB Veyseh, J Ebrahimi, D Dou, D Lowd - Proceedings of the 2017 ACM …, 2017 - dl.acm.org
Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017dl.acm.org
Rumor stance classification is the task of determining the stance towards a rumor in text.
This is the first step in effective rumor tracking on social media which is an increasingly
important task. In this work, we analyze Twitter users' stance toward a rumorous tweet, in
which users could support, deny, query, or comment upon the rumor. We propose a deep
attentional CNN-LSTM approach, which takes the sequence of tweets in a thread of
conversation as the input. We use neighboring tweets in the timeline as context vectors to …
Rumor stance classification is the task of determining the stance towards a rumor in text. This is the first step in effective rumor tracking on social media which is an increasingly important task. In this work, we analyze Twitter users' stance toward a rumorous tweet, in which users could support, deny, query, or comment upon the rumor. We propose a deep attentional CNN-LSTM approach, which takes the sequence of tweets in a thread of conversation as the input. We use neighboring tweets in the timeline as context vectors to capture the temporal dynamism in users' stance evolution. In addition, we use extra features such as friendship, to leverage useful relational features that are readily available in social media. Our model achieves the state-of-the-art results on rumor stance classification on a recent SemEval dataset, improving accuracy and F1 score by 3.6% and 4.2% respectively.
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