Tools that are able to detect unverified information posted on social media during a news event can help to avoid the spread of rumours that turn out to be false. In this paper we compare a novel approach using Conditional Random Fields that learns from the sequential dynamics of social media posts with the current state-of-the-art rumour detection system, as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying the stance of a post to deem it a rumour but, instead, exploits context learned during the event. Our classifier has improved precision and recall over the state-of-the-art classifier that relies on querying tweets, as well as outperforming our best baseline. Moreover, the results provide evidence for the generalisability of our classifier.udud
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机译：能够检测新闻事件期间发布在社交媒体上的未经验证信息的工具可以帮助避免谣传的传播，而谣言的传播是错误的。在本文中，我们比较了一种使用条件随机场的新颖方法，该方法从社交媒体帖子的顺序动态，当前最新的谣言检测系统以及其他基准中学习。与现有工作相反，我们的分类器不需要观察推文来查询帖子的立场以将其视为谣言，而是利用事件中学习到的上下文。与依靠查询推文的最新分类器相比，我们的分类器具有更高的精度和召回率，并且优于我们的最佳基准。此外，结果为我们的分类器的通用性提供了证据。 ud ud
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