CUET ’s_ W hite_ W alkers@ LT - EDI 2025: Racial Hoax Detection in Code-Mixed on Social Media Data (2025)

Abstract: False narratives that manipulate racial tensions are increasingly prevalent on social media, often blending languages and cultural references to enhance reach and believability. Among them, racial hoaxes produce unique harm by fabricating events targeting specific communities, social division and fueling misinformation. This paper presents a novel approach to detecting racial hoaxes in code-mixed Hindi-English social media data. Using a carefully constructed training pipeline, we have fine-tuned the XLM-RoBERTa-base multilingual transformer for training the shared task data. Our approach has incorporated task-specific preprocessing, clear methodology, and extensive hyperparameter tuning. After developing our model, we tested and evaluated it on the LT-EDI@LDK 2025 shared task dataset. Our system achieved the highest performance among all the international participants with an F1-score of 0.75, ranking 1st on the official leaderboard.

## introduction racial hoaxes on social networks have continuously emerged as a significant concern, which may lead to increased ethnic tension and social unrest. in this study, we define hoax speech as intentional, deceptive linguistic content that mimics the tone or structure of hate speech or propaganda, yet lacks genuine hateful intent. it often uses irony, satire, or fabricated narratives to incite confusion or mask malicious undertones for a criticise race. racial hoaxes refer to fabricated statements that falsely accuse specific racial groups with malicious intent. these intents are designed to manipulate public opinion and provoke communal or ethnic tension. according to amnesty international in the news minute, racial hoaxes and hate speech have become prevalent in south asia day by day, leading to violence and social turmoil. for instance, between 2015 and 2019, india witnessed 902 alleged hate crimes resulting in 303 deaths, with muslims constituting the majority of victims foot_0 . social media can amplify such crimes, leading to hatred, misinformation for the minority group or community, especially. a significant number of previous studies have been conducted on spreading misinformation, fake news, and hate speech detection @xcite , @xcite , but the task of racial hoax detection has not been explored too much. the primary objective of this paper is to detect hindi-english code-mixed racially deceptive text on social media platforms. we have used a transformer-based model, hing-roberta, which is pre-trained on hindi-english code-mixed data, to train our model. the core contributions of our research work are as follows:-1. we have developed an effective model to detect racial deception in hindi-english codemixed text. the implementation details have been provided in the following github repository:- https://github.com/mizan116/lt-edi-ldk2025/racial hoax . ## related study hate speech detection identifies degrading information, especially on social media. hate speech, sexism, homophobia, racism, bullying, and other verbal abuse are detected. prior work has studied the online dissemination of racially based stereotypes and disinformation. however, very few studies examine racial hoaxes through multilingual lenses @xcite . initially, the discipline was dominated by classical machine learning algorithms such as support vector machines (svms) and naïve bayes and random forests for text mining techniques. the authors of @xcite used machine learning techniques to detect hoaxes in the english language. rule-based machine learning methods have also been used in @xcite . they have been used for detecting hate speech in hindi-english code-mixed text. research across multiple languages shows how racial hoaxes and stereotypes circulate in social media conversations in @xcite . in a related shared task on dravidian languages, the authors of rahman et al. (2025) employed transformer models like xlm-r and muril, demonstrating high performance in abusive language detection. the authors of @xcite have detected hateful users more accurately and fairly, including social network context. on the other hand, @xcite researched how adolescents show confidence in spotting fake news, but often fail to detect or remember racial hoaxes. according to @xcite , the integration of social network data contributes to improved performance and equity in classification systems. the dearth of developed critical thinking skills exposes teenagers to greater deception. adolescents often feel immune to fake news despite being unable to identify or remember it @xcite . the biradar et al. (2024b) introduced a novel dataset with hindi-english code-mixed dataset of hateful comments, aiming to explore the link between fake narratives and hate severity. it is problematic to identify hate speech in codeswitched languages such as hinglish because of its intricacy. some social network analyses have applied a focus on features of social media such as usual name-calling but study of bias elimination and diversity linguistics is scant @xcite . ## dataset & task overview we have utilized the abusive detection dataset from the lt-edi@ldk 2025 shared task @xcite . this research makes use of a codemixed hindi-english dataset meant for detecting racial hoaxes in social media posts. the dataset is publicly available in chakravarthi ( 2020 ) research. we used their dataset to train, test and evaluate our model. the dataset is divided into three parts: training, development, and test. the test set does not have labels. our models predicted the labels for that set. the dataset has two 'non-hoax' (0) and 'racial hoax' (1) annotations per sample. we have training (3060 samples), validation (1021 samples), and test (1021 samples). around each text sample, there is a word count of 29-30. the dataset suffers from class imbalance (76% -24%) as the non-hoax class dominates strongly over the racial hoax class. the distribution has been demonstrated in ## methodology this section provides an overview of the methodology and approach that have been used to build the system using the previously described dataset and transformer model. the methodology of our work is shown in figure 2. ## result and error analysis in this section, we have compared the results and analyzed the different transformer's performance based on the evaluation metrics. the macro f1score measures the supremacy of the models. table-3 shows the evaluation metrics for our best model. ## conclusion in this study, we proposed a transformer-based classification pipeline for detecting racial hoaxes in code-mixed hindi-english social media content. our approach incorporated robust preprocessing, hinglish-specific tokenization, and fine-tuned multilingual models such as xlm-roberta. due to the problem of class imbalance in the dataset, we placed additional emphasis on preprocessing, incorporating techniques such as oversampling, data augmentation to mitigate the skew and enhance model generalization. that is why the experimental results demonstrated the effectiveness of our methodology, achieving strong performance on the lt-edi@ldk-2025 shared task. among all international participants, our system secured first place in the shared task with a satisfactory f1-score of 0.75, demonstrating the effectiveness of our method in addressing racially manipulative content in multilingual contexts. these findings highlight the importance of addressing racial disinformation in multilingual online spaces using deep learning.