@inproceedings{subramanian-etal-2025-kec-elite-analysts,
title = "{KEC}-Elite-Analysts@{LT}-{EDI} 2025: Leveraging Deep Learning for Racial Hoax Detection in Code-Mixed {H}indi-{E}nglish Tweets",
author = "Subramanian, Malliga and
A, Aruna and
M, Amudhavan and
S, Jahaganapathi and
Shanmugavadivel, Kogilavani",
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-1.19/",
pages = "111--115",
ISBN = "978-88-6719-334-9",
abstract = "Detecting misinformation in code-mixed languages, particularly Hindi-English, poses significant challenges in natural language processing due to the linguistic diversity found on social media. This paper focuses on racial hoax detection{---}false narratives that target specific communities{---}within Hindi-English YouTube comments. We evaluate the effectiveness of several machine learning models, including Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Multi-Layer Perceptron, using a dataset of 5,105 annotated comments. Model performance is assessed using accuracy, precision, recall, and F1-score. Experimental results indicate that neural and ensemble models consistently outperform traditional classifiers. Future work will explore the use of transformer-based architectures and data augmentation techniques to enhance detection in low-resource, code-mixed scenarios."
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<abstract>Detecting misinformation in code-mixed languages, particularly Hindi-English, poses significant challenges in natural language processing due to the linguistic diversity found on social media. This paper focuses on racial hoax detection—false narratives that target specific communities—within Hindi-English YouTube comments. We evaluate the effectiveness of several machine learning models, including Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Multi-Layer Perceptron, using a dataset of 5,105 annotated comments. Model performance is assessed using accuracy, precision, recall, and F1-score. Experimental results indicate that neural and ensemble models consistently outperform traditional classifiers. Future work will explore the use of transformer-based architectures and data augmentation techniques to enhance detection in low-resource, code-mixed scenarios.</abstract>
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%0 Conference Proceedings
%T KEC-Elite-Analysts@LT-EDI 2025: Leveraging Deep Learning for Racial Hoax Detection in Code-Mixed Hindi-English Tweets
%A Subramanian, Malliga
%A A, Aruna
%A M, Amudhavan
%A S, Jahaganapathi
%A Shanmugavadivel, Kogilavani
%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 subramanian-etal-2025-kec-elite-analysts
%X Detecting misinformation in code-mixed languages, particularly Hindi-English, poses significant challenges in natural language processing due to the linguistic diversity found on social media. This paper focuses on racial hoax detection—false narratives that target specific communities—within Hindi-English YouTube comments. We evaluate the effectiveness of several machine learning models, including Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Multi-Layer Perceptron, using a dataset of 5,105 annotated comments. Model performance is assessed using accuracy, precision, recall, and F1-score. Experimental results indicate that neural and ensemble models consistently outperform traditional classifiers. Future work will explore the use of transformer-based architectures and data augmentation techniques to enhance detection in low-resource, code-mixed scenarios.
%U https://aclanthology.org/2025.ltedi-1.19/
%P 111-115
Markdown (Informal)
[KEC-Elite-Analysts@LT-EDI 2025: Leveraging Deep Learning for Racial Hoax Detection in Code-Mixed Hindi-English Tweets](https://aclanthology.org/2025.ltedi-1.19/) (Subramanian et al., LTEDI 2025)
ACL
- Malliga Subramanian, Aruna A, Amudhavan M, Jahaganapathi S, and Kogilavani Shanmugavadivel. 2025. KEC-Elite-Analysts@LT-EDI 2025: Leveraging Deep Learning for Racial Hoax Detection in Code-Mixed Hindi-English Tweets. In Proceedings of the 5th Conference on Language, Data and Knowledge: Fifth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 111–115, Naples, Italy. Unior Press.