  False rumors are known to have detrimental effects on society. To prevent the
spread of false rumors, social media platforms such as Twitter must detect them
early. In this work, we develop a novel probabilistic mixture model that
classifies true vs. false rumors based on the underlying spreading process.
Specifically, our model is the first to formalize the self-exciting nature of
true vs. false retweeting processes. This results in a novel mixture marked
Hawkes model (MMHM). Owing to this, our model obviates the need for feature
engineering; instead, it directly models the spreading process in order to make
inferences of whether online rumors are incorrect. Our evaluation is based on
13,650 retweet cascades of both true. vs. false rumors from Twitter. Our model
recognizes false rumors with a balanced accuracy of 64.97% and an AUC of
69.46%. It outperforms state-of-the-art baselines (both neural and feature
engineering) by a considerable margin but while being fully interpretable. Our
work has direct implications for practitioners: it leverages the spreading
process as an implicit quality signal and, based on it, detects false content.
