@inproceedings{yadav-singh-2025-dll5143a-lt,
title = "{D}ll5143{A}@{LT}-{EDI} 2025: Bias-Aware Detection of Racial Hoaxes in Code-Mixed Social Media Data ({B}a{C}o{H}oax)",
author = "Yadav, Ashok and
Singh, Vrijendra",
editor = "Gkirtzou, Katerina and
{\v{Z}}itnik, Slavko and
Gracia, Jorge and
Gromann, Dagmar and
di Buono, Maria Pia and
Monti, Johanna and
Ionov, Maxim",
booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = sep,
year = "2025",
address = "Naples, Italy",
publisher = "Unior Press",
url = "https://aclanthology.org/2025.ltedi-1.6/",
pages = "31--38",
ISBN = "978-88-6719-334-9",
abstract = "The proliferation of racial hoaxes that associate individuals or groups with fabricated crimes or incidents presents unique challenges in multilingual social media contexts. This paper introduces BaCoHoax, a novel framework for detecting race-based misinformation in code-mixed content. We address this problem by participating in the ``Shared Task Detecting Racial Hoaxes in Code-Mixed Hindi-English Social Media Data: LT-EDI@LDK 2025.'' BaCoHoax is a bias-aware detection system built on a DeBERTa-based architecture, enhanced with disentangled attention mechanisms, a dynamic bias discovery module that adapts to emerging narrative patterns, and an adaptive contrastive learning objective. We evaluated BaCoHoax on the HoaxMixPlus corpus, a collection of 5,105 YouTube comments annotated for racial hoaxes, achieved a competitive macro F1 score of 0.67 and securing 7th place among participating teams in the shared task.Our findings contribute to the growing field of multilingual misinformation detection and highlight the importance of culturally informed approaches to identifying harmful content in linguistically diverse online spaces."
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<abstract>The proliferation of racial hoaxes that associate individuals or groups with fabricated crimes or incidents presents unique challenges in multilingual social media contexts. This paper introduces BaCoHoax, a novel framework for detecting race-based misinformation in code-mixed content. We address this problem by participating in the “Shared Task Detecting Racial Hoaxes in Code-Mixed Hindi-English Social Media Data: LT-EDI@LDK 2025.” BaCoHoax is a bias-aware detection system built on a DeBERTa-based architecture, enhanced with disentangled attention mechanisms, a dynamic bias discovery module that adapts to emerging narrative patterns, and an adaptive contrastive learning objective. We evaluated BaCoHoax on the HoaxMixPlus corpus, a collection of 5,105 YouTube comments annotated for racial hoaxes, achieved a competitive macro F1 score of 0.67 and securing 7th place among participating teams in the shared task.Our findings contribute to the growing field of multilingual misinformation detection and highlight the importance of culturally informed approaches to identifying harmful content in linguistically diverse online spaces.</abstract>
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%0 Conference Proceedings
%T Dll5143A@LT-EDI 2025: Bias-Aware Detection of Racial Hoaxes in Code-Mixed Social Media Data (BaCoHoax)
%A Yadav, Ashok
%A Singh, Vrijendra
%Y Gkirtzou, Katerina
%Y Žitnik, Slavko
%Y Gracia, Jorge
%Y Gromann, Dagmar
%Y di Buono, Maria Pia
%Y Monti, Johanna
%Y Ionov, Maxim
%S Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2025
%8 September
%I Unior Press
%C Naples, Italy
%@ 978-88-6719-334-9
%F yadav-singh-2025-dll5143a-lt
%X The proliferation of racial hoaxes that associate individuals or groups with fabricated crimes or incidents presents unique challenges in multilingual social media contexts. This paper introduces BaCoHoax, a novel framework for detecting race-based misinformation in code-mixed content. We address this problem by participating in the “Shared Task Detecting Racial Hoaxes in Code-Mixed Hindi-English Social Media Data: LT-EDI@LDK 2025.” BaCoHoax is a bias-aware detection system built on a DeBERTa-based architecture, enhanced with disentangled attention mechanisms, a dynamic bias discovery module that adapts to emerging narrative patterns, and an adaptive contrastive learning objective. We evaluated BaCoHoax on the HoaxMixPlus corpus, a collection of 5,105 YouTube comments annotated for racial hoaxes, achieved a competitive macro F1 score of 0.67 and securing 7th place among participating teams in the shared task.Our findings contribute to the growing field of multilingual misinformation detection and highlight the importance of culturally informed approaches to identifying harmful content in linguistically diverse online spaces.
%U https://aclanthology.org/2025.ltedi-1.6/
%P 31-38
Markdown (Informal)
[Dll5143A@LT-EDI 2025: Bias-Aware Detection of Racial Hoaxes in Code-Mixed Social Media Data (BaCoHoax)](https://aclanthology.org/2025.ltedi-1.6/) (Yadav & Singh, LTEDI 2025)
ACL