  We present a data-driven method for determining the veracity of a set of
rumorous claims on social media data. Tweets from different sources pertaining
to a rumor are processed on three levels: first, factuality values are assigned
to each tweet based on four textual cue categories relevant for our journalism
use case; these amalgamate speaker support in terms of polarity and commitment
in terms of certainty and speculation. Next, the proportions of these lexical
cues are utilized as predictors for tweet certainty in a generalized linear
regression model. Subsequently, lexical cue proportions, predicted certainty,
as well as their time course characteristics are used to compute veracity for
each rumor in terms of the identity of the rumor-resolving tweet and its binary
resolution value judgment. The system operates without access to
extralinguistic resources. Evaluated on the data portion for which hand-labeled
examples were available, it achieves .74 F1-score on identifying rumor
resolving tweets and .76 F1-score on predicting if a rumor is resolved as true
or false.
