Exploring the Usability of Persuasion Techniques for Downstream Misinformation-related Classification Tasks
We systematically explore the predictive power of features derived from Persuasion Techniques detected in texts, for solving different tasks of interest for media analysis; notably: detecting mis/disinformation, fake news, propaganda, partisan news and conspiracy theories. Firstly, we propose a set of meaningful features, aiming to capture the persuasiveness of a text. Secondly, we assess the discriminatory power of these features in different text classification tasks on 8 selected datasets from the literature using two metrics. We also evaluate the per-task discriminatory
power of each Persuasion Technique and report on different insights. We find out that most of these features have a noticeable potential to distinguish conspiracy theories, hyperpartisan news and propaganda, while we observed mixed results in the context of fake news detection.
NIKOLAIDIS Nikolaos;
PISKORSKI Jakub;
STEFANOVITCH Nicolas;
2024-10-30
ELRA Language Resource Association
JRC135415
https://aclanthology.org/2024.lrec-main.613.pdf,
https://publications.jrc.ec.europa.eu/repository/handle/JRC135415,
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