D ll5143 A @ LT - EDI 2025: Bias-Aware Detection of Racial Hoaxes in Code-Mixed Social Media Data ( B a C o H oax) (2025)

Abstract: The proliferation of racial hoaxes that associate individuals or groups with fabricated crimes or incidents presents unique challenges in multilingual social media contexts. This paper introduces BaCoHoax, a novel framework for detecting race-based misinformation in code-mixed content. We address this problem by participating in the “Shared Task Detecting Racial Hoaxes in Code-Mixed Hindi-English Social Media Data: LT-EDI@LDK 2025.” BaCoHoax is a bias-aware detection system built on a DeBERTa-based architecture, enhanced with disentangled attention mechanisms, a dynamic bias discovery module that adapts to emerging narrative patterns, and an adaptive contrastive learning objective. We evaluated BaCoHoax on the HoaxMixPlus corpus, a collection of 5,105 YouTube comments annotated for racial hoaxes, achieved a competitive macro F1 score of 0.67 and securing 7th place among participating teams in the shared task.Our findings contribute to the growing field of multilingual misinformation detection and highlight the importance of culturally informed approaches to identifying harmful content in linguistically diverse online spaces.

## introduction the proliferation of social networks has transformed the way information spreads between various linguistic communities, creating both opportunities and challenges for marginalized populations around the world ( @xcite ; @xcite ). during recent global events, including the covid-19 pandemic, online platforms have become critical spaces for emotional expression and support seeking @xcite . for vulnerable communities such as women in stem, lgbtiq individuals, racial minorities, and people with disabilities. these digital spaces significantly influence self-perception and social integration @xcite ; @xcite ; @xcite . while numerous studies have focused on detecting negative content through hate speech recognition @xcite , offensive language identification @xcite , and implicit hate detection @xcite , these approaches often fail to address underlying biases and may inadvertently discriminate against minority groups. the spread of misinformation targeting specific communities presents a particularly concerning challenge, especially in multilingual contexts where traditional detection systems struggle to operate effectively. this is especially problematic in code-mixed environments where individuals switch between multiple languages within single utterances (chakravarthi, 2020), creating linguistic patterns that evade conventional detection methods. while research has expanded to include languages beyond english, including arabic, german, hindi, and italian, these studies primarily examine monolingual corpora, overlooking the complexity of code-switched communication prevalent in diverse linguistic regions like south asia. the key contributions of our work include: (1) a deberta-based architecture enhanced with disentangled attention mechanisms specifically optimized for code-mixed content; (2) a dynamic bias discovery component that continuously identifies potentially biased terms throughout the training process, adapting to emerging narrative patterns; and (3) an adaptive contrastive learning approach that enhances the model's ability to distinguish subtle patterns in misinformation. through comprehensive evaluation and error analysis, we demonstrate both the effectiveness of our approach. code: 1 2 task and dataset descriptions the shared task lt-edi@ldk 2025 focused on the detection of racial hoaxes in code-mixed hindi-english content, addressing the critical challenge of misinformation targeting specific social or ethnic communities @xcite . the primary objective of the shared task was to develop computational systems capable of automatically identifying such content in low-resource, multilingual contexts. participating teams were required to build binary classification systems that could distinguish between regular content (non-hoax) and racial-based misinformation (hoax) in codemixed text, where speakers switch between hindi and english within the same utterance. the task evaluation employed macro-averaged f1 score as the primary metric, which equally weights performance on both the majority (non-hoax) and minority (hoax) classes, thereby encouraging systems to effectively identify the less frequent but more harmful racial hoax content. the task organizers provided the hoaxmixplus, a corpus comprising 5,105 youtube comment annotated for racial hoaxes, as shown in ## proposed methodology in this section, we present bacohoax, a novel framework for detecting racial hoaxes in codemixed content. our approach extends the capabilities of pretrained language models by integrating bias-aware components specifically designed to identify and leverage linguistic patterns associated with hoaxes. the framework consists of four main components: (1) a deberta-based encoder, (2) a dynamic bias discovery mechanism, (3) disentangled bias-aware attention, and (4) a contrastive learning objective. we formulate the task as a supervised classification problem. given a text input @xmath0, x 2 , ..., x n } consisting of n tokens, our goal is to predict whether it contains a racial hoax, represented as a binary label @xmath1, 1}, where 1 indicates the presence of hoax. ## experimental settings and result analysis we implement our model using pytorch and the transformers library. for the deberta encoder, we use the microsoft/deberta-v3-base variant with 12 layers and a hidden size of 768. we configure training with a maximum sequence length of 128 tokens and a batch size of 8. the adamw optimizer is configured with a base learning rate of 2 × 10 -6 and a maximum learning rate of 1 × 10 -5 , with a weight decay of 0.01. the model is trained for 10 epochs, using early stopping based on the validation f1 score. for the bias discovery component, we use a bias threshold of 0.65 and a minimum occurrence count of 5. the contrastive learning weight is initialized at 0.15 and dynamically adjusted during training within the range [0.05, 0.3]. all experiments were performed using a nvidia a30 gpu. the performance of our bacohoax framework was evaluated in the context of the shared task, where systems were primarily ranked based on their macro-averaged f1-scores. ## conclusion and future work in this paper, we introduced bacohoax, a novel framework for detecting race based hoax content in code-mixed content that leverages disentangled attention mechanisms and bias-aware representations. our approach incorporates cultural and linguistic nuances through a dynamic bias discovery mechanism and contextual understanding of bias terms in multilingual settings. we obatined a competitive macro f1 score of 0.67 and securing 7th place among participating teams in the shared task. the pre-defined bias term approach creates problematic overgeneralization and identity-bias conflation, where the model cannot effectively distinguish between merely mentioning an identity group and expressing bias toward that group.the imapct of pre-deifned bias terms are discussed in detail a.4. these findings suggest the need for context-aware and culturally grounded bias detection methods, along with improved discourse analysis and refined lexicon strategies. future models should also consider multi-stage architectures to better handle the complexity of code-mixed and nuanced biased content.