@inproceedings{kotonya-toni-2019-gradual,
    title = "Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection",
    author = "Kotonya, Neema  and
      Toni, Francesca",
    editor = "Stein, Benno  and
      Wachsmuth, Henning",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-4518/",
    doi = "10.18653/v1/W19-4518",
    pages = "156--166",
    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."
}