W o NB ias: A Dataset for Classifying Bias & Prejudice Against Women in B engali Text (2025)

Abstract: This paper presents WoNBias, a curated Bengali dataset to identify gender-based biases, stereotypes, and harmful language directed at women. It merges digital sources- social media, blogs, news- with offline tactics comprising surveys and focus groups, alongside some existing corpora to compile a total of 31,484 entries (10,656 negative; 10,170 positive; 10,658 neutral). WoNBias reflects the sociocultural subtleties of bias in both Bengali digital and offline conversations. By bridging online and offline biased contexts, the dataset supports content moderation, policy interventions, and equitable NLP research for Bengali, a low-resource language critically underserved by existing tools. WoNBias aims to combat systemic gender discrimination against women on digital platforms, empowering researchers and practitioners to combat harmful narratives in Bengali-speaking communities.

## introduction to provide essential context for our work, it is crucial first to understand the linguistic landscape of bengali (bangla), an indo-european, indo-aryan language primarily spoken in bangladesh and west bengal, india. while mutually intelligible, regional variations exist, with this paper focusing on the bangladeshi variety. gender bias and negative gender discourse against women on digital platforms in bengali often escape detection because of the linguistic inability of universal tools and fragmented moderation infrastructure. with its flexible grammar and rich corpus of idioms, bengali offers a source of subtle stereotypes as well as hate speech that can persist, especially in informal online discourse. there are so far not enough data, specifically to detect bias in language with low resource potential, such as bengali, which means that online discrimination against women cannot be detected by automated content filters. further, we present wonbias, a dataset of 31,484 annotated texts from social medias, news platforms, blogs, offline surveys, and focus groups. the dataset includes entries in negative (2), positive (1), and neutral(0) categories, respectively, to explore the sociocultural context of bengali texts. our study highlights the need for language-specific resources to contribute towards better content moderation, training equitably effective language models in bengali, and combating discriminatory behavior towards women in social media. ## related work recent work has focused on identifying and reducing the biases present in large language models (llms), with benchmark datasets playing a key role in that effort. one notable example is bold @xcite , a dataset and evaluation framework designed to surface stereotypes in open-ended text generation across domains like gender, race, profession, religion, and politics in english. by comparing model-generated text to wikipedia-derived prompts, bold shows that llms often produce more biased or toxic content than human writers, highlighting the need for more responsible generative systems. more recently, banstereoset @xcite introduced a culturally grounded benchmark for bengali, with 1,194 sentences covering nine categories such as race, profession, and religion. this dataset helps reveal how multilingual llms carry over or even amplify localized social biases, especially in underrepresented languages. together, these resources stress the importance of culturally diverse benchmarks when evaluating model fairness. underlying all these biases is the data these models are trained on. studies show that stereotypes in training data often get reinforced by llms-such as associating certain professions with specific genders or favoring dominant religious narratives even after corrective prompting @xcite . this problem also shows up in multilingual contexts. for example, llms tend to default to western views even when responding to prompts rooted in arab culture, a bias made clear through the camel dataset of culturally grounded arabic prompts @xcite . in low-resource languages like bengali, progress is being made but challenges remain. banglabert @xcite , trained on a large, diverse collection of bengali texts, has improved language understanding, but the model has no focused objective for eliminating discriminatory texts. for instance, the sent-nob @xcite dataset shows that handcrafted features often outperform deep models when dealing with informal bengali text. this points to a clear need for richer, context-aware datasets that reflect the diversity of the bengali language and culture. without intentional effort, llms risk repeating the same biases we wish to move past. ## model training and results we fine-tuned the banglabert-model model 6 for bias classification on our dataset comprising three classes: neutral, positive, and negative. the evaluation metrics focused on per-class recall (accuracy), as shown in figure 1 . the model achieved the highest recall for the neutral class (0.962), followed by positive (0.881), and negative (0.824). to further analyze model performance, we provide the normalized confusion matrix in figure 2 . the classifier shows relatively stronger performance in distinguishing neutral and positive classes, while the negative class exhibits more confusion-most notably being misclassified as neutral (10.65%). while the overall performance is promising, we acknowledge that the classifier struggles more with the 6 banglabert-wonbias-genderbiasandprejudiceclassifier negative class. this version of the model serves as a foundational baseline for further improvements in the classification of bias against women in bengali. future work will explore class imbalance handling, richer contextual embeddings, and domain-specific fine-tuning to mitigate these limitations. ## limitations & future plans the dataset presents several limitations: it primarily focuses on binary gender bias, overlooking non-binary identities and intersectional discrimination, thus limiting broader applicability. furthermore, its lack of contextual bias detection means keyword-based methods struggle with implicit or culturally coded biases like sarcasm. lastly, the absence of onomastic analysis prevents distinguishing gendered names or analyzing related biases, limiting insights into subtle job associations. in future work, we aim to pursue several avenues, including cross-linguistic expansion of wonbias to other south asian languages (e.g., urdu, hindi) for comparative gender bias analysis. we also aim for enhanced dialect coverage, incorporating local dialects (e.g., sylheti, chittagonian) to explore bias variations across linguistic subcultures. further, developing a bias severity scale to classify intensity (mild stereotypes to hate speech) would enable targeted content moderation. finally, model benchmarking on won-bias would assess various language models' effectiveness in addressing gender bias. ## conclusion this paper presents wonbias, a comprehensive 31,484-entry annotated bengali text dataset for detecting gender bias against women in digital discourse. sourced diversely (social media, news, blogs, direct participant engagement), we have created a resource that captures the complex linguistic patterns of gender bias specific to the bengali language and culture. the dataset's balanced distribution across the categories provides a solid foundation for training and evaluating bias detection systems. this paper addresses a critical gap in low-resource language nlp by providing a culturally grounded benchmark for bias detection in bengali. our annotation process achieved substantial interannotator agreement (κ = 0.74), demonstrating the reliability of the dataset despite challenges in distinguishing between subtle forms of bias, particularly in the positive-neutral boundary cases. the extensive lexical diversity captured in wonbias, with 52,671 unique tokens and over 60 bengali feminine terms, ensures comprehensive coverage of gender-related discourse. while acknowledging limitations, we are hopeful that our future work will incorporate dialect-specific annotations, develop nuanced bias severity classifications, and enhance contextual understanding capabilities to detect increasingly subtle forms of linguistic discrimination.