IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 465–469, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics IKM at SemEval-2017 Task 8: Convolutional Neural Networks for Stance Detection and Rumor Verification Yi-Chin Chen, Zhao-Yang Liu, Hung-Yu Kao Department of Computer Science and Information Engineering National Cheng Kung University Tainan, Taiwan, ROC {kimberycc,kjes89011}@gmail.com hykao@mail.ncku.edu.tw Abstract This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1st among all scores on sub- task B. 1 Introduction Rumors in social networks are widely noticed due to the broad success of online social media. Un- confirmed rumors usually spark discussion before being verified. These have created cost for society and panic among people. Rather than relying on human observers to identify trending rumors, it would be helpful to detect them automatically and limit the damage immediately. However, identify- ing false rumors early is a hard task without suffi- cient evidence such as responses, retweet and fact checking sites. Instead of propagation structure, context-level patterns are more obvious and useful for the identification of rumors at this stage – in particular, observing the different patterns of stances amongst participants (Qazvinian et al., 2011). Recent research has proposed a 4-way classifi- cation task to encompass all the different kinds of reactions to rumors (Arkaitz et al., 2016). A sche- ma of classifications including supporting, deny- ing, querying and commenting (SDQC) is applied in SemEval2017 Task 8. In this paper, we describe a system for stance classification and rumor verification in tweets. For the first task, we are given tree-structured conver- sations, where replies are triggered by a source tweet. We need to categorize the replies into one of the SDQC categories by reply-source pairs. The second task is about rumor verification. Our sys- tem is for the closed variant – which means the veracity of a rumor will have to be predicted sole- ly without external data. It is a challenging NLP task. Statements con- taining sarcasm, irony and metaphor often need personal experience to be able to infer their broader context (Kreuz and Caucci, 2007). Fur- thermore, lots of background knowledge is re- quired to do the fact checking (Reichel and Lendvai, 2016). In this paper, we develop convolutional neural network models for both tasks. Our system relies on a supervised classifier, using text features of different word representation methods such as learning word embedding through training and pre-trained word embedding model like GloVe (Pennington et al., 2014). The experiment section presents our results and discusses the performance of our work. 2 Related Work Rumor verification from online social media has developed into a popular subject in recent years. The most common features were proposed by Castillo …

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