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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.

Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection