  Kyle (1985) proposes two types of rumors: informed rumors which are based on
some private information and uninformed rumors which are not based on any
information (i.e. bluffing). Also, prior studies find that when people have
credible source of information, they are likely to use a more confident textual
tone in their spreading of rumors. Motivated by these theoretical findings, we
propose a double-channel structure to determine the ex-ante veracity of rumors
on social media. Our ultimate goal is to classify each rumor into true, false,
or unverifiable category. We first assign each text into either certain
(informed rumor) or uncertain (uninformed rumor) category. Then, we apply lie
detection algorithm to informed rumors and thread-reply agreement detection
algorithm to uninformed rumors. Using the dataset of SemEval 2019 Task 7, which
requires ex-ante threefold classification (true, false, or unverifiable) of
social media rumors, our model yields a macro-F1 score of 0.4027, outperforming
all the baseline models and the second-place winner (Gorrell et al., 2019).
Furthermore, we empirically validate that the double-channel structure
outperforms single-channel structures which use either lie detection or
agreement detection algorithm to all posts.
