Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study
This work addresses rumor verification for social media users by augmenting evidence sources, though it is incremental as it builds on existing verification systems.
The paper tackles the problem of rumor verification by introducing a new task to detect the stance of authorities towards rumors in Arabic tweets, constructing and releasing the first dataset for this purpose. They found existing stance detection datasets are somewhat useful but insufficient, motivating the need for annotated authority stance data.
A myriad of studies addressed the problem of rumor verification in Twitter by either utilizing evidence from the propagation networks or external evidence from the Web. However, none of these studies exploited evidence from trusted authorities. In this paper, we define the task of detecting the stance of authorities towards rumors in tweets, i.e., whether a tweet from an authority agrees, disagrees, or is unrelated to the rumor. We believe the task is useful to augment the sources of evidence utilized by existing rumor verification systems. We construct and release the first Authority STance towards Rumors (AuSTR) dataset, where evidence is retrieved from authority timelines in Arabic Twitter. Due to the relatively limited size of our dataset, we study the usefulness of existing datasets for stance detection in our task. We show that existing datasets are somewhat useful for the task; however, they are clearly insufficient, which motivates the need to augment them with annotated data constituting stance of authorities from Twitter.