Hinterwelt@ LT - EDI 2025: A Transformer-Based Approach for Identifying Racial Hoaxes in Code-Mixed H indi- E nglish Social Media Narratives (2025)

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.

## introduction communication has undergone massive changes since the use of social media in the current world. people all across the globe can connect with each other and social media platforms such as youtube have made this form of communication easier than ever. any form of communication these days has its fair share of issues. misinformation is a prime example of its misuse, with racial hoaxes being a type that poses a significant threat to social cohesion. racial hoaxes falsely link individuals or groups to crimes or incidents, perpetuate and reinforce harmful stereotypes and accelerate ethnic tensions. in india's multi-language setting, the informal blending of hindi and english on the in-ternet creates a code-mixed environment which makes such identification difficult because of the informal usage, linguistic complexity and limited datasets. the lt-edi 2025 task introduces a dataset (chakravarthi, 2020), comprising 5,105 youtube comments in code-mixed hindi-english, annotated as racial hoax or non-racial hoax. the dataset addresses the scarcity of resources for misinformation detection in low-resource, code-mixed settings. the critical contributions of this work are: existing literature focuses on issues of identifying abusive content in code switched hindi-english social media texts. patil et al. (2023) presented hing-bert, a model pre-trained on code-mixed data, which performs better than the non domain-specific models for tasks such as hateful content detection, pointing to the relevance of domain specific models to learn linguistic nuances. for hate speech, which is related to racial hoax identification, mazumder et al. (2024) showed that the addition of native hate samples improved the multilingual language model as more training data, but not for subjective or sarcastic which is a general characteristic of racial hoax. s et al. (2024) used ensemble stacked model with xlm-roberta model for tamil code-mix content, with a weighted f1-score of 0.72. their method using lstm and gru with multilangual embeddings may also be applied to hindi-english contexts. in an attempt to account for sarcasm in racial hoaxes, sahu and r (2024) introduced a model, based on bert embeddings and contextual embeddings, which is able to detect sarcasm and irony within code-mixed social media data. in the case of low-resource languages like dravidian languages, hande et al. (2021) employed a sample of pseudo-labeling techniques to augment the dataset, and their fine-tuned ulmfit model scored competitive results with a technique which may be used for hindi-english racial hoax detection. kumar et al. (2023) also investigated offensive text classification using mbert-like transformer based models, for some indian languages with traditional approaches and achieved competitive f1-scores up to 0.95 in malayalam, demonstrating that transformers can capture code-mixed text well. anbukkarasi et al. (2022) worked on tamil-english hate speech detection using a synonym-based bi-lstm model and tackled standardization problems for indian languages. these experiments demonstrate the effectiveness of multilingual embeddings, domainspecific pre-training, and data augmentation. our future work will fine-tune models such as hing-bert on racial hoax specific datasets and gain true evaluation metrics to avoid lopsided performance. ## task and dataset description we present our system for the shared task for the task of identifying racial hoaxes present in the code-mixed hindi-english social media narratives @xcite . it addresses the growing threat of online disinformation by focusing in on racial hoaxes, false claims about a crime or incident that pin the blame on someone or a group based on race, create harm and increase discord in communities affected. we used the hoaxmixplus dataset (chakravarthi, 2020), a novel collection of 5,102 youtube comments curated and annotated for this tricky phenomenon. this annotated corpus that has been pre-processed and split into training (3,060 samples), dev (1,021 samples) and test (1,021 samples), is one of the few such resources which is highly valuable for detecting this type of manipulative content in a low-resource, codemixed linguistic environment. the comments are divided binarily (hoax/non-hoax), and their objective is to report on the performance of systems. ## methodology this section describes the methodologies used for the the text classification from code-mixed hindi-english social media data. a wide variety of models ranging from traditional machine learning ones to deep learning architectures and modern transformer-based methods were evaluated and systematically fine-tuned through hyperparameter optimization to improve classification performance. the architectural frameworks utilized for the detection of racial hoaxes is illustrated in figure ## conclusion the paper introduced a complete framework for detecting racial hoaxes in code-mixed hindi-english social media narrative. from the experimental results, it can be concluded that transformerbased models such as the hingroberta-mixed are particularly effective, with the best f1 score at 0.7505, significantly exceeding conventional machine learning and deep learning algorithms. the error analysis shows difficulties in identifying implicit bias and the context subtleties in novel sophisticated hoaxes. these results suggest the necessity of custom language model for code-mixes in combating this pressing problem of online disinformation. in the future, we would like to improve identification of implicit racial sentiments, including more context features and developing ensemble methods to achieve model adaptability across such diversity of linguistic expressions in these low-resource environments.