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.