Hope_for_best@ LT - EDI 2025: Detecting Racial Hoaxes in Code-Mixed H indi- E nglish Social Media Data using a multi-phase fine-tuning strategy (2025)

Abstract: In the age of digital communication, social media platforms have become a medium for the spread of misinformation, with racial hoaxes posing a particularly insidious threat. These hoaxes falsely associate individuals or communities with crimes or misconduct, perpetuating harmful stereotypes and inflaming societal tensions. This paper describes the team “Hope_for_best” submission that addresses the challenge of detecting racial hoaxes in codemixed Hindi-English (Hinglish) social media content and secured the 2nd rank in the shared task (Chakravarthi et al., 2025). To address this challenge, the study employs the HoaxMix Plus dataset, developed by LT-EDI 2025, and adopts a multi-phase fine-tuning strategy. Initially, models are sensitized using the THAR dataset—targeted hate speech against religion (Sharma et al., 2024) —to adjust weights toward contextually relevant biases. Further fine-tuning was performed on the HoaxMix Plus dataset. This work employed data balancing sampling strategies to mitigate class imbalance. Among the evaluated models, Hing BERT achieved the highest macro F1-score of 73% demonstrating promising capabilities in detecting racially charged misinformation in code-mixed Hindi-English texts.

## introduction in the digital era, social media platforms have revolutionized global communication by enabling individuals to disseminate information across vast and diverse audiences. however, this accessibility has also facilitated the rapid spread of misinformation, including racially charged hoaxes that falsely implicate individuals or communities in criminal or unethical behavior. such hoaxes are not merely misinformative but are deliberately crafted to reinforce harmful stereotypes, incite hostility, and exacerbate societal divides @xcite . platforms like twitter, facebook, and youtube empower users with the ability to express opinions publicly and anonymously. while this democratization of speech has positive implications, it also opens the door to misuse. these platforms, with their anonymous nature and rapid content spread, often intensify hate-fueled narratives. this makes it increasingly important to build automated systems that can identify and curb such content before it leads to real-world consequences @xcite . in the domain of targeted hate speech detection, natural language processing (nlp) has made significant progress with the advent of deep learning architectures @xcite . early approaches employed long short-term memory networks (lstm) @xcite and gated recurrent units (gru) @xcite , but the emergence of transformerbased models, particularly bert @xcite , has significantly improved performance across a range of language understanding tasks. one of the major challenges in this domain is dealing with code-mixed text, especially hindi-english (hinglish), which is commonly used in indian social media discourse. this linguistic mixing complicates tokenization, syntactic parsing, and semantic understanding. while several studies have focused on hate speech detection in indic languages @xcite , the problem of detecting racial hoaxes in hinglish remains underexplored. the present study contributes to this growing field by introducing a transformer-based approach for the classification of racial hoaxes in code-mixed hindi-english social media content. building on the hoaxmixplus dataset introduced by lt-edi 2025, this study employs a multi-phase fine-tuning strategy to adapt models for the detection of contextually biased misinformation. initially, pretraining is conducted on the thar dataset @xcite , which targets hate speech against religious communities, followed by fine-tuning on task-specific hoaxmixplus data. among the models evaluated, hing-bert achieved the best performance, demonstrating its effectiveness in capturing racially hostile content embedded in informal, code-mixed linguistic structures. this paper is structured as follows: section 2 provides a comprehensive review of the existing literature on misinformation and hate speech detection, specifically within the context of code-mixed languages. section 3 outlines the datasets utilized in this study, namely the thar and hoaxmix-plus datasets, along with a detailed description of their respective features. section 4 describes the methodology employed, with an emphasis on the multi-phase fine-tuning approach, model architecture, and data preparation techniques. in section 5, the experimental results are presented. section 6 offers a detailed discussion and analysis of the model's performance. finally, section 7 concludes the paper and highlights potential avenues for future research. ## related work the detection of misinformation and racially motivated hoaxes in social media has attracted increasing attention in recent years, particularly in multilingual and code-mixed contexts. prior studies have explored various linguistic and contextual challenges in identifying harmful narratives, such as hate speech, fake news, and racially biased misinformation. there is a growing need for research addressing harmful and biased content in code-mixed and multilingual social media, supported by the creation of linguistically diverse datasets and model strategies. studies such as hopeedi (chakravarthi, 2020) and the ensemble-based model for hope speech detection in english and dravidian languages @xcite highlight the effectiveness of such approaches in promoting inclusive and equitable language technologies. code-mixed language, especially hindi-english (hinglish), presents significant challenges for natural language understanding due to its informal structure and lack of standardized grammar. recent efforts, such as @xcite , have addressed sentiment analysis and offensive language detection in code-mixed texts through semeval-2020. similarly, @xcite provided foundational insights into part-of-speech tagging 1 https://www.aclweb.org/anthology/2020.peoples-1.5/ in bengali-hindi-english code-mixed social media content, underscoring the complexity of such texts. although racial hoaxes remain an underexplored domain, prior research in hate speech detection provides valuable foundations. datasets such as thar @xcite target religious hate in multilingual indian contexts, offering pretraining potential for models tackling similar sociolinguistic phenomena. other notable works include @xcite , who introduced hatexplain-a benchmark dataset for hate speech detection with multiperspective annotations including class labels, target communities, and human-provided rationales to improve model explainability and reduce bias, and @xcite , who developed a multi-class classifier to distinguish between non-islamophobic, weakly islamophobic, and strongly islamophobic content, emphasizing the need for nuanced categorization over binary classification. transfer learning through sequential fine-tuning has shown considerable promise in improving taskspecific performance for low-resource and domainspecific problems. the use of a multiphase finetuning pipeline, where models are initially exposed to related bias-aligned data (e.g., hate speech or religious hostility) and subsequently adapted to the target task (e.g., racial hoaxes), aligns with strategies explored in @xcite , who demonstrated the efficacy of domain-adaptive pretraining. in the current work, models such as hing-bert leverage this approach by first calibrating on thar before task-specific tuning on hoaxmixplus. pretrained multilingual models like muril @xcite and domain-specific transformers such as hing-bert @xcite have been specifically optimized for indian languages and their mixed variations. these models benefit from pretraining on diverse scripts and colloquial structures, making them suitable for nuanced detection tasks in hinglish texts. moreover, models like hing-roberta-mixed have demonstrated competitive performance in identifying hate speech in informal, noisy, and multilingual settings. given the skewed nature of real-world social media datasets, strategies like data sampling, loss re-weighting, and oversampling are commonly adopted to mitigate bias and improve minority class detection. approaches documented in @xcite emphasize the importance of balancing in achieving fairer performance across all classes. despite the substantial progress in detecting hate speech, fake news, and offensive content in codemixed and multilingual contexts, the specific problem of identifying racially motivated hoaxes remains insufficiently addressed. most existing studies focus on broad categories of harmful content, often overlooking the nuanced linguistic and contextual markers unique to racial hoaxes, especially in informal, code-mixed languages like hinglish. this presents a significant research gap, as racially charged misinformation can have far-reaching societal impacts. in this work, we aim to address this gap by proposing a novel multi-phase fine-tuning approach-first sensitizing models on a related hate speech dataset (thar), then adapting them to the task-specific hoaxmixplus dataset for racial hoax detection. this strategy enhances model performance in low-resource settings while introducing a focused lens on racial misinformation. ## dataset description the datasets used in this work were provided by the organizers of the lt-edi 2025 shared task 1 @xcite . two datasets were employed in our multi-phase fine-tuning approach: the thar dataset @xcite , which targets religion-based hate speech, and the hoaxmix-plus dataset, a novel resource annotated for racial hoaxes in code-mixed hindi-english social media content. ## results the proposed approach, centered around hing-bert and refined using a multi-phase fine-tuning strategy, demonstrated strong effectiveness in identifying racial hoaxes in code-mixed hindi-english (hinglish) social media data. after initially adapting the model to socio-religious hate contexts using the thar dataset, the system was further finetuned on the hoaxmixplus dataset, allowing it to capture task-specific linguistic and contextual cues. on the test set consisting of 1,021 instances, the model achieved a high overall accuracy of 80%, affirming the robustness of the learned representations. notably, the model attained a macroaveraged f1-score of 0.73, indicating balanced performance across both hoax and non-hoax classes. the weighted average precision and recall values, both reaching 0.81 and 0.80 respectively, highlight the model's strong capability to make reliable predictions while handling class distribution effectively. the baai bge-m3 model also performed well, with a macro f1-score of 0.72 and accuracy of 0.79, closely followed by muril and hing-robertamixed. models like indic-bert, hing-bert lid @xcite and roberta-en-hicodemixed exhibited comparatively lower scores, suggesting they are less effective for this specific task. the results affirm that domain-specific pretraining and code-mixed adaptability significantly enhance model effectiveness for this challenge. the results of all the models evaluated using the training data are presented in the results presented in ## discussion hing-bert outperforms other models in detecting racial hoaxes within code-mixed hindi-english social media text due to its alignment with the linguistic characteristics of such data. unlike models like indic-bert, mbert, or muril, which were pre-trained on formal or monolingual corpora, hing-bert was pre-trained on large-scale real-world code-mixed data from platforms such as twitter and youtube. this exposure enables it to model code-switching patterns, transliteration variants (e.g., acha, accha, achha), and the blending of grammatical structures across languages more effectively.a key strength is its ability to deal with the informal, messy nature of social media, including slang, spelling variations, hashtags, emojis, and subtle code-switch points that may signal sarcasm or misinformation. however, the model has its drawbacks. it tends to favor the majority class due to dataset imbalance and can be sensitive to noisy inputs like excessive emojis or special characters. there's also the risk of hidden biases linked to demographics or dialects in the training data. finally, the model's decisions are not easily explainable, making it harder to understand or trust why certain posts are flagged as racial hoaxes. ## conclusion and future work this study presents an effective multi-phase finetuning approach for detecting racial hoaxes in hinglish social media content, achieving a macro f1-score of 73% using hing-bert. by incorporating bias-aware pretraining via the thar dataset and addressing class imbalance through strategic sampling, the model demonstrates enhanced contextual sensitivity and robustness. future enhancements may include using contrastive learning @xcite to better identify subtle forms of hate, incorporating other types of information such as images and hashtags, and expanding the system to support more code-mixed languages spoken in india and fairness audits and bias mitigation to improve reliability. techniques like adversarial testing @xcite and explainability @xcite can also help make the model more reliable and easier to understand when used in real-world settings.