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        <title>Beyond the Surface: Spurious Cues in Automatic Media Bias Detection</title>
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            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
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    <abstract>This study investigates the robustness and generalization of transformer-based models for automatic media bias detection. We explore the behavior of current bias classifiers by analyzing feature attributions and stress-testing with adversarial datasets. The findings reveal a disproportionate focus on rare but strongly connotated words, suggesting a rather superficial understanding of linguistic bias and challenges in contextual interpretation. This problem is further highlighted by inconsistent bias assessment when stress-tested with different entities and minorities. Enhancing automatic media bias detection models is critical to improving inclusivity in media, ensuring balanced and fair representation of diverse perspectives.</abstract>
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%0 Conference Proceedings
%T Beyond the Surface: Spurious Cues in Automatic Media Bias Detection
%A Wessel, Martin
%A Horych, Tomáš
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F wessel-horych-2024-beyond
%X This study investigates the robustness and generalization of transformer-based models for automatic media bias detection. We explore the behavior of current bias classifiers by analyzing feature attributions and stress-testing with adversarial datasets. The findings reveal a disproportionate focus on rare but strongly connotated words, suggesting a rather superficial understanding of linguistic bias and challenges in contextual interpretation. This problem is further highlighted by inconsistent bias assessment when stress-tested with different entities and minorities. Enhancing automatic media bias detection models is critical to improving inclusivity in media, ensuring balanced and fair representation of diverse perspectives.
%U https://aclanthology.org/2024.ltedi-1.3
%P 21-30

Markdown (Informal)

[Beyond the Surface: Spurious Cues in Automatic Media Bias Detection](https://aclanthology.org/2024.ltedi-1.3) (Wessel & Horych, LTEDI-WS 2024)

• Beyond the Surface: Spurious Cues in Automatic Media Bias Detection (Wessel & Horych, LTEDI-WS 2024)

ACL

• Martin Wessel and Tomáš Horych. 2024. Beyond the Surface: Spurious Cues in Automatic Media Bias Detection. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 21–30, St. Julian's, Malta. Association for Computational Linguistics.