eventAI at SemEval-2019 Task 7: Rumor Detection on Social Media by Exploiting Content, User Credibility and Propagation Information

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 855–859 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 855 . Abstract This paper describes our system for SemEval 2019 RumorEval: Determining rumor veracity and support for rumors (SemEval 2019 Task 7). This track has two tasks: Task A is to determine a user’s stance towards the source rumor, and Task B is to detect the veracity of the rumor: true, false or unverified. For stance classification, a neural network model with language features is utilized. For rumor verification, our approach exploits information from different dimensions: rumor content, source credibility, user credibility, user stance, event propagation path, etc. We use an ensemble approach in both tasks, which includes neural network models as well as the traditional classification algorithms. Our system is ranked 1st place in the rumor verification task by both the macro F1 measure and the RMSE measure. 1 Introduction Social media platforms, such as Twitter, Reddit and Facebook, do not always poses authentic information. Rumors sometimes may spread quickly over these platforms (Castillo et al., 2011; Derczynski and Bontcheva, 2014; Qazvinian et al., 2011). A rumor may be defined as a statement whose truth value is unverified or deliberately false (Qazvinian et al., 2011). Rumors usually spread fear or even euphoria, and they may confuse people and cause them to make wrong decisions. Therefore, rumor detection has gained great interest recently. In this paper, we describe the approaches we used in SemEval 2019 RumourEval: Determining rumor veracity and support for rumors (SemEval 2019 Task 7). This task has two subtasks: Task A - user stance classification and Task B - rumor verification. Stance classification is to determine the attitude of the author of a post message towards a target (Mohammad et al., 2016). In task A, we focus on stance classification of messages towards the truthfulness of rumors in Twitter or Reddit conversations. Each conversation is defined by a source post that initiates the conversation, and a set of replies to it, which form a conversation thread. The goal is to classify each post into one of the four categories: supporting, denying, querying or commenting (SDQC). For this task, we use an ensemble approach, which combines the prediction results from both the traditional learning models, such as SVM, and a neural network model, using the language features extracted from the message text. Task B predicts the veracity of a rumor: true, false, or unverified (i.e., its veracity cannot be verified based on the given information). Each rumor consists of a source post that makes a claim, and a set of replies, directly or indirectly towards the source post. We also employed an ensemble approach on this task, which uses multiple models together to do the veracity prediction. For more details about these two tasks, please check the task description paper from the task organizers (Gorrell et al., 2019). 2 Related Studies Rumor detection on social media has been a popular research topic in recent …

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