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

本文提出了一个用于自动检测谣言立场和真实性的多任务学习框架，通过对社交媒体上的巨量数据进行聚类和分析，解决了传统处理方法效率低下的问题，实验证明该方法在 SemEval 2019 的谣言立场分类和真实性预测都优于之前的方法。