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    <abstract>This paper presents our system for the detection of racial hoaxes in code-mixed Hindi-English social media narratives, which is in reality a form of debunking of online disinformation claiming fake incidents against a racial group. We experiment with different modeling techniques on HoaxMixPlus dataset of 5,102 annotated YouTube comments. In our approach, we utilize traditional machine learning classifiers (SVM, LR, RF), deep learning models (CNN, CNN-LSTM, CNN-BiLSTM), and transformer-based architectures (MuRIL, XLM-RoBERTa, HingRoBERTa-mixed). Experiments show that transformer-based methods substantially outperform traditional approaches, and the HingRoBERTa-mixed model is the best one with an F1 score of 0.7505. An error analysis identifies the difficulty of recognizing implicit bias and nuanced contexts in complex hoaxes. Our team was 5th place in the challenge with an F1 score of 0.69. This work contributes to combating online misinformation in low-resource linguistic environments and highlights the effectiveness of specialized language models for code-mixed content.</abstract>
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%0 Conference Proceedings
%T Hinterwelt@LT-EDI 2025: A Transformer-Based Approach for Identifying Racial Hoaxes in Code-Mixed Hindi-English Social Media Narratives
%A Rahman, Md. Abdur
%A Amin, Md. Al
%A Aftahee, Sabik
%A Rahman, Md. Ashiqur
%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 rahman-etal-2025-hinterwelt
%X This paper presents our system for the detection of racial hoaxes in code-mixed Hindi-English social media narratives, which is in reality a form of debunking of online disinformation claiming fake incidents against a racial group. We experiment with different modeling techniques on HoaxMixPlus dataset of 5,102 annotated YouTube comments. In our approach, we utilize traditional machine learning classifiers (SVM, LR, RF), deep learning models (CNN, CNN-LSTM, CNN-BiLSTM), and transformer-based architectures (MuRIL, XLM-RoBERTa, HingRoBERTa-mixed). Experiments show that transformer-based methods substantially outperform traditional approaches, and the HingRoBERTa-mixed model is the best one with an F1 score of 0.7505. An error analysis identifies the difficulty of recognizing implicit bias and nuanced contexts in complex hoaxes. Our team was 5th place in the challenge with an F1 score of 0.69. This work contributes to combating online misinformation in low-resource linguistic environments and highlights the effectiveness of specialized language models for code-mixed content.
%U https://aclanthology.org/2025.ltedi-1.21/
%P 121-126

Markdown (Informal)

[Hinterwelt@LT-EDI 2025: A Transformer-Based Approach for Identifying Racial Hoaxes in Code-Mixed Hindi-English Social Media Narratives](https://aclanthology.org/2025.ltedi-1.21/) (Rahman et al., LTEDI 2025)

• Hinterwelt@LT-EDI 2025: A Transformer-Based Approach for Identifying Racial Hoaxes in Code-Mixed Hindi-English Social Media Narratives (Rahman et al., LTEDI 2025)

ACL

• Md. Abdur Rahman, Md. Al Amin, Sabik Aftahee, and Md. Ashiqur Rahman. 2025. Hinterwelt@LT-EDI 2025: A Transformer-Based Approach for Identifying Racial Hoaxes in Code-Mixed Hindi-English Social Media Narratives. In Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 121–126, Naples, Italy. Unior Press.