KEC -Elite-Analysts@ LT - EDI 2025: Leveraging Deep Learning for Racial Hoax Detection in Code-Mixed H indi- E nglish Tweets (2025)

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.

## introduction racial hoax detection in nlp focuses on identifying false narratives that target specific communities. the rise of social media has intensified this issue, particularly in hindi-english code-mixed text, where language switching and informal usage are common @xcite . traditional misinformation detection models face challenges with such multilingual, low-resource data. this study addresses the problem using the hoaxmixplus dataset from the lt-edi@ldk 2025 shared task on racial hoaxes. we explore machine learning approaches including logistic regression, svm, random forest, naive bayes, and mlp. experimental results highlight the effectiveness of ensemble and neural models in identifying racial hoaxes, contributing to safer online discourse in multilingual spaces. ## literature survey racial hoax detection in hindi-english code-mixed social media text presents unique challenges due to informal language, code-switching, and sociocultural sensitivity @xcite . earlier approaches using traditional machine learning models like logistic regression and svm showed limitations in capturing the nuanced context and implicit bias present in hoax-related content. these models often struggled with language ambiguity and inconsistent grammar patterns in code-mixed data. neural models such as mlps and ensemblebased methods like random forest improved performance by learning better feature representations. however, detecting racially motivated misinformation still remains difficult due to lack of annotated datasets and subtle narrative framing. the shared task at lt-edi@ldk 2025 introduced a benchmark dataset to address these gaps, encouraging research focused on robust detection strategies for code-mixed racial hoaxes. the overview paper presents the task setup, dataset characteristics, evaluation metrics, and a comparative analysis @xcite of the approaches adopted by participating systems. ## materials and methods this study focuses on identifying racial hoaxes in hindi-english code-mixed social media text. the dataset used, hoaxmixplus, comprises 3,060 annotated comments from youtube. racial hoaxes are challenging to detect due to implicit stereotyping, multilinguality, and informal language use (not specified, 2021). the dataset is manually annotated, balanced across hoax and non-hoax classes, and preprocessed for modeling. multiple machine learning models were employed, including logistic regression, random forest, support vector machine (svm), naive bayes, and multi-layer perceptron (mlp). performance was evaluated using accuracy, precision, recall, and f1-score. ## results and discussion this study on racial hoax detection in hindi-english code-mixed text demonstrated that traditional machine learning models, particularly the multi-layer perceptron (mlp), performed effectively when paired with proper preprocessing and feature extraction techniques. compared to simpler models like naive bayes and logistic regression, mlp consistently achieved better accuracy due to its capacity to learn complex patterns. the best performance was observed with tf-idf features and optimized hyperparameters, yielding an accuracy in the range of 78-80 evaluation metrics including precision, recall, and macro-averaged f1-score showed that while simpler models could identify obvious hoaxes, they often misclassified nuanced or indirect expressions. mlp, in contrast, handled these challenges better, demonstrating the value of deep, feedforward architectures even in limited-resource, code-mixed scenarios. ## conclusion this study addressed the challenge of detecting racial hoaxes in code-mixed hindi-english social media content using the dataset, a collection of 5,105 annotated youtube comments. we evaluated traditional machine learning models logistic regression, random forest, svm, and naive bayes alongside a deep learning mlp model, which achieved the highest performance by effectively capturing subtle identity based misinformation patterns. the results highlight the importance of tailored approaches for low-resource, code-mixed data where misinformation can have serious social implications. future work will focus on expanding the dataset, incorporating transformer-based models, and optimizing hybrid architectures for improved performance. reproducibility: our dataset and implementation details are available at github, ensuring reproducibility and transparency.