@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection
%A Kotonya, Neema
%A Toni, Francesca
%Y Stein, Benno
%Y Wachsmuth, Henning
%S Proceedings of the 6th Workshop on Argument Mining
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F kotonya-toni-2019-gradual
%X 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.
%R 10.18653/v1/W19-4518
%U https://aclanthology.org/W19-4518
%U https://doi.org/10.18653/v1/W19-4518
%P 156-166
Markdown (Informal)
[Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection](https://aclanthology.org/W19-4518) (Kotonya & Toni, ArgMining 2019)
ACL