  The utilization of social media material in journalistic workflows is
increasing, demanding automated methods for the identification of mis- and
disinformation. Since textual contradiction across social media posts can be a
signal of rumorousness, we seek to model how claims in Twitter posts are being
textually contradicted. We identify two different contexts in which
contradiction emerges: its broader form can be observed across independently
posted tweets and its more specific form in threaded conversations. We define
how the two scenarios differ in terms of central elements of argumentation:
claims and conversation structure. We design and evaluate models for the two
scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to
represent claims and conversation structure implicitly in a generic inference
model, while previous studies used explicit or no representation of these
properties. To address noisy text, our classifiers use simple similarity
features derived from the string and part-of-speech level. Corpus statistics
reveal distribution differences for these features in contradictory as opposed
to non-contradictory tweet relations, and the classifiers yield state of the
art performance.
