  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.
