SemEval-2019任务7:RumourEval,确定谣言的真实性和支持谣言

Genevieve Gorrell, E. Kochkina, Maria Liakata, Ahmet Aker, A. Zubiaga, Kalina Bontcheva, Leon Derczynski
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引用次数: 42

摘要

自2017年RumourEval首次共享任务以来,随着“假新闻”的危险成为主流担忧,人们对自动索赔验证的兴趣大大增加。然而,对谣言验证的自动化支持仍处于起步阶段。因此,重要的是,在这一领域的共同任务继续为可能增加的努力提供一个重点。谣言验证的特点是需要考虑不断发展的对话和新闻更新,以对谣言的真实性做出判断。与RumourEval 2017一样,我们提供了一个社交媒体上可疑帖子和随后对话的数据集,并对立场和真实性进行了注释。社交媒体上的谣言源于各种突发新闻故事,数据集扩展到包括Reddit和Twitter的新帖子。有两项具体任务;谣言立场预测和谣言验证,我们详细介绍了参与者取得的结果。我们收到了22份系统提交(比RumourEval 2017增加了70%),其中许多使用了最先进的方法来解决所涉及的挑战。
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SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the danger of “fake news” has become a mainstream concern. However automated support for rumour verification remains in its infancy. It is therefore important that a shared task in this area continues to provide a focus for effort, which is likely to increase. Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour’s veracity. As in RumourEval 2017 we provided a dataset of dubious posts and ensuing conversations in social media, annotated both for stance and veracity. The social media rumours stem from a variety of breaking news stories and the dataset is expanded to include Reddit as well as new Twitter posts. There were two concrete tasks; rumour stance prediction and rumour verification, which we present in detail along with results achieved by participants. We received 22 system submissions (a 70% increase from RumourEval 2017) many of which used state-of-the-art methodology to tackle the challenges involved.
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