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A SHAP-based controversy analysis through communities on Twitter

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Abstract

Controversy encompasses content that draws diverse perspectives, along with positive and negative feedback on a specific event, resulting in the formation of distinct user communities. we explore the explainability of controversy through the lens of SHAP (SHapley Additive exPlanations) method, aiming to provide a fair assessment of the individual contributions of different text features of tweets to controversy detection. We conduct an analysis of topic discussions on Twitter from a community perspective, investigating the role of text in accurately classifying tweets into their respective communities. To achieve this, we introduce a SHAP-based pipeline designed to quantify the influence of impactful text features on the predictions of three tweet classifiers. Text content alone offers interesting controversy detection accuracy. It can contain predictive features for controversy detection. For instance, negative connotations, pejorative tendencies and positive qualifying adjectives tend to impact the controversy model detection.

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Data Availability

No datasets were generated or analysed during the current study.

Notes

  1. https://www.liwc.app/

  2. https://pypi.org/project/deep-translator/

  3. a “perfect” feature would represent 2 well-separated clusters of colors, far away from the decision boundary.

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Funding

This work was supported by grants from Janssen Horizon endowment fund.

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Authors and Affiliations

  1. LIRMM, Univ Montpellier, CNRS, Montpellier, France

    Samy Benslimane, Jérôme Azé, Sandra Bringay & Maximilien Servajean

  2. LIPN UMR 7030, University of Sorbonne Paris Nord, 93430, Villetaneuse, France

    Thomas Papastergiou

  3. AMIS, Paul-Valéry University, Montpellier, France

    Sandra Bringay & Maximilien Servajean

  4. Institut du Cancer Montpellier (ICM), Montpellier, France

    Caroline Mollevi

  5. IDESP, UMR Inserm - Univ Montpellier, Montpellier, France

    Caroline Mollevi

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  1. Samy Benslimane
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Contributions

Thomas Papastergiou worked on the statistical part, and wrote sections 3.4.1 and 5.4.1. Samy Benslimane worked and wrote the remaining of the manuscript text. All authors reviewed the manuscript.

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Correspondence to Samy Benslimane.

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This article belongs to the Topical Collection: MEDES-IDEAS 2023

Guest Editors: Joe Tekli, Djamal Benslimane, Richard Chbeir, Yannis Manolopoulos and Ngoc-Thanh Nguyen

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Benslimane, S., Papastergiou, T., Azé, J. et al. A SHAP-based controversy analysis through communities on Twitter. World Wide Web 27, 65 (2024). https://doi.org/10.1007/s11280-024-01278-z

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