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Conference Papers Year : 2023

Explaining controversy through community analysis on Twitter

Explication de la controverse par l'analyse des communautés sur Twitter

Abstract

Controversy refers to content attracting different point-of-views, as well as positive and negative feedback on a specific event, gathering users into different communities. Research on controversy led to two main categories of works: controversy detection/quantification and controversy explainability. When the former aims to quantify controversy on a topic, the latter aims to understand why a topic is controversial or not. This paper mainly contributes to the controversy explainability. We analyze topic discussions on Twitter from the community perspective to investigate the power of text in classifying tweets into the right community. We propose a SHAP-based pipeline to quantify impactful text features on predictions of three tweet classifiers. We also rely on the use of different text features namely BERT, TF − IDF, and LIWC. The results we obtain from both SHAP plots and statistical analysis show clearly significant impacts of some text features in classifying tweets.It also highlights the relevance of the study as well as the potential benefits of combining text and user interactions to quantify controversy.
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Dates and versions

lirmm-04185786 , version 1 (23-08-2023)

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Samy Benslimane, Thomas Papastergiou, Jérôme Azé, Sandra Bringay, Caroline Mollevi, et al.. Explaining controversy through community analysis on Twitter. IDEAS 2023 - 27th International Database Engineered Applications Symposium Conference, May 2023, Heraklion, Greece. pp.148-155, ⟨10.1145/3589462.3589480⟩. ⟨lirmm-04185786⟩
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