论文标题
微调长构造者，用于共同预测谣言和真实性
Fine-Tune Longformer for Jointly Predicting Rumor Stance and Veracity
论文作者
论文摘要
社交媒体的使用增加引起了新闻和事件的普及，这些新闻和事件甚至没有得到验证，从而导致谣言传播。由于广泛可用的社交媒体平台并增加了使用，使数据大量可用。处理如此大的数据的手动方法是昂贵且耗时的，因此，人们对流程的关注越来越高，并自动验证此类内容以在谣言的存在中自动验证。许多研究表明，在确定谣言真实性之前，在此类事件和新闻的讨论线程中确定帖子的立场是一个重要的步骤。在本文中，我们提出了一个多任务学习框架，以在Semeval 2019年发布的数据集上共同预测谣言和真实性：确定谣言的真实性和对谣言的支持（Semeval 2019 Task 7）（Semeval 2019 Task 7），其中包括社交媒体谣言，其中包括来自Reddit和Twit-twit-twit-ter-ter-Twit-ter的各种突发新闻。我们的框架由两个部分组成：a）我们框架的底部对对话线程中的每个帖子的立场进行了分类，这些帖子通过对多转交谈进行建模，并使每个帖子都知道其相邻的帖子，从而讨论了谣言。 b）上部预测了对话线的谣言真实性，其立场演变从底部获得。 Semeval 2019任务7数据集的实验结果表明，我们的方法在谣言立场分类和真实性预测上都优于以前的方法
Increased usage of social media caused the popularity of news and events which are not even verified, resulting in spread of rumors allover the web. Due to widely available social media platforms and increased usage caused the data to be available in huge amounts.The manual methods to process such large data is costly and time-taking, so there has been an increased attention to process and verify such content automatically for the presence of rumors. A lot of research studies reveal that to identify the stances of posts in the discussion thread of such events and news is an important preceding step before identify the rumor veracity. In this paper,we propose a multi-task learning framework for jointly predicting rumor stance and veracity on the dataset released at SemEval 2019 RumorEval: Determining rumor veracity and support for rumors(SemEval 2019 Task 7), which includes social media rumors stem from a variety of breaking news stories from Reddit as well as Twit-ter. Our framework consists of two parts: a) The bottom part of our framework classifies the stance for each post in the conversation thread discussing a rumor via modelling the multi-turn conversation and make each post aware of its neighboring posts. b) The upper part predicts the rumor veracity of the conversation thread with stance evolution obtained from the bottom part. Experimental results on SemEval 2019 Task 7 dataset show that our method outperforms previous methods on both rumor stance classification and veracity prediction
