Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection

Neema Kotonya, Francesca Toni


Abstract
Stance detection plays a pivot role in fake news detection. The task involves determining the point of view or stance – for or against – a text takes towards a claim. One very important stage in employing stance detection for fake news detection is the aggregation of multiple stance labels from different text sources in order to compute a prediction for the veracity of a claim. Typically, aggregation is treated as a credibility-weighted average of stance predictions. In this work, we take the novel approach of applying, for aggregation, a gradual argumentation semantics to bipolar argumentation frameworks mined using stance detection. Our empirical evaluation shows that our method results in more accurate veracity predictions.
Anthology ID:
W19-4518
Volume:
Proceedings of the 6th Workshop on Argument Mining
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | ArgMining | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
156–166
Language:
URL:
https://www.aclweb.org/anthology/W19-4518
DOI:
10.18653/v1/W19-4518
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/W19-4518.pdf