GenAI4ELab/papercli-papers huggingface dataset:
CUET-NLP_Big_O@DravidianLangTech 2025: A BERT-based Approach to Detect Fake News from Malayalam Social Media Texts
Authors: Nazmus Sakib; Md. Refaj Hossan; Alamgir Hossain; Jawad Hossain; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The rapid growth of digital platforms and social media has significantly contributed to spreading fake news, posing serious societal challenges. While extensive research has been conducted on detecting fake news in high-resource languages (HRLs) such as English, relatively little attention has been given to low-resource languages (LRLs) like Malayalam due to insufficient data and computational tools. To address this challenge, the DravidianLangTech 2025 workshop organized a shared task on fake news detection in Dravidian languages. The task was divided into two sub-tasks, and our team participated in Task 1, which focused on classifying social media texts as original or fake. We explored a range of machine learning (ML) techniques, including Logistic Regression (LR), Multinomial Naïve Bayes (MNB), and Support Vector Machines (SVM), as well as deep learning (DL) models such as CNN, BiLSTM, and a hybrid CNN+BiLSTM. Additionally, this work examined several transformer-based models, including m-BERT, Indic-BERT, XLM-Roberta, and MuRIL-BERT, to exploit the task. Our team achieved 6th place in Task 1, with MuRIL-BERT delivering the best performance, achieving an F1 score of 0.874.
LexiLogic@DravidianLangTech 2025: Detecting Fake News in Malayalam and AI-Generated Product Reviews in Tamil and Malayalam
Authors: Souvik Bhattacharyya; Pranav Gupta; Niranjan Kumar M; Billodal Roy
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Fake news and hard-to-detect AI-generated content are pressing issues in online media, which are expected to exacerbate due to the recent advances in generative AI. Moreover, tools to keep such content under check are less accurate for languages with less available online data. In this paper, we describe our submissions to two shared tasks at the NAACL Dravidian Language Tech workshop, namely detecting fake news in Malayalam and detecting AI-generated product reviews in Malayalam and Tamil. We obtained test macro F1 scores of 0.29 and 0.82 in the multi-class and binary classification sub-tasks within the Malayalam fake news task, and test macro F1 scores of 0.9 and 0.646 in the task of detecting AI-generated product reviews in Malayalam and Tamil respectively.
SSNTrio @ DravidianLangTech 2025: Hybrid Approach for Hate Speech Detection in Dravidian Languages with Text and Audio Modalities
Authors: J Bhuvana; Mirnalinee T T; Rohan R; Diya Seshan; Avaneesh Koushik
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: This paper presents the approach and findings from the Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL) shared task at DravidianLangTech@NAACL 2025. The task focuses on detecting multimodal hate speech in Tamil, Malayalam, and Telugu, requiring models to analyze both text and speech components from social media content. The proposed methodology uses language-specific BERT models for the provided text transcripts, followed by multimodal feature extraction techniques, and classification using a Random Forest classifier to enhance performance across the three languages. The models achieved a macro-F1 score of 0.7332 (Rank 1) in Tamil, 0.7511 (Rank 1) in Malayalam, and 0.3758 (Rank 2) in Telugu, demonstrating the effectiveness of the approach in multilingual settings. The models performed well despite the challenges posed by limited resources, highlighting the potential of language-specific BERT models and multimodal techniques in hate speech detection for Dravidian languages.
Fired_from_NLP@DravidianLangTech 2025: A Multimodal Approach for Detecting Misogynistic Content in Tamil and Malayalam Memes
Authors: Md. Sajid Alam Chowdhury; Mostak Mahmud Chowdhury; Anik Mahmud Shanto; Jidan Al Abrar; Hasan Murad
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: In the context of online platforms, identifying misogynistic content in memes is crucial for maintaining a safe and respectful environment. While most research has focused on high-resource languages, there is limited work on languages like Tamil and Malayalam. To address this gap, we have participated in the Misogyny Meme Detection task organized by DravidianLangTech@NAACL 2025, utilizing the provided dataset named MDMD (Misogyny Detection Meme Dataset), which consists of Tamil and Malayalam memes. In this paper, we have proposed a multimodal approach combining visual and textual features to detect misogynistic content. Through a comparative analysis of different model configurations, combining various deep learning-based CNN architectures and transformer-based models, we have developed fine-tuned multimodal models that effectively identify misogynistic memes in Tamil and Malayalam. We have achieved an F1 score of 0.678 for Tamil memes and 0.803 for Malayalam memes.
One_by_zero@DravidianLangTech 2025: Fake News Detection in Malayalam Language Leveraging Transformer-based Approach
Authors: Dola Chakraborty; Shamima Afroz; Jawad Hossain; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The rapid spread of misinformation in the digital era presents critical challenges for fake news detection, especially in low-resource languages (LRLs) like Malayalam, which lack extensive datasets and pre-trained models for widely spoken languages. This gap in resources makes it harder to build robust systems for combating misinformation despite the significant societal and political consequences it can have. To address these challenges, this work proposes a transformer-based approach for Task 1 of the Fake News Detection in Dravidian Languages (DravidianLangTech@NAACL 2025), which focuses on classifying Malayalam social media texts as either original or fake. The experiments involved a range of ML techniques (Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT)) and DL architectures (BiLSTM, BiLSTM-LSTM, and BiLSTM-CNN). This work also explored transformer-based models, including IndicBERT, MuRiL, XLM-RoBERTa, and Malayalam BERT. Among these, Malayalam BERT achieved the best performance, with the highest macro F1-score of 0.892, securing a rank of 3rd in the competition.
CUET_Novice@DravidianLangTech 2025: A Multimodal Transformer-Based Approach for Detecting Misogynistic Memes in Malayalam Language
Authors: Khadiza Sultana Sayma; Farjana Alam Tofa; Md Osama; Ashim Dey
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Memes, combining images and text, are a popular social media medium that can spread humor or harmful content, including misogyny—hatred or discrimination against women. Detecting misogynistic memes in Malayalam is challenging due to their multimodal nature, requiring analysis of both visual and textual elements. A Shared Task on Misogyny Meme Detection, organized as part of DravidianLangTech@NAACL 2025, aimed to address this issue by promoting the advancement of multimodal machine learning models for classifying Malayalam memes as misogynistic or non-misogynistic. In this work, we explored visual, textual, and multimodal approaches for meme classification. CNN, ResNet50, Vision Transformer (ViT), and Swin Transformer were used for visual feature extraction, while mBERT, IndicBERT, and MalayalamBERT were employed for textual analysis. Additionally, we experimented with multimodal fusion models, including IndicBERT+ViT, MalayalamBERT+ViT, and MalayalamBERT+Swin. Among these, our MalayalamBERT+Swin Transformer model performed best, achieving the highest weighted F1-score of 0.87631, securing 1st place in the competition. Our results highlight the effectiveness of multimodal learning in detecting misogynistic Malayalam memes and the need for robust AI models in low-resource languages.
teamiic@DravidianLangTech2025-NAACL 2025: Transformer-Based Multimodal Feature Fusion for Misogynistic Meme Detection in Low-Resource Dravidian Language
Authors: Harshita Sharma; Simran; Vajratiya Vajrobol; Nitisha Aggarwal
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Misogyny has become a pervasive issue in digital spaces. Misleading gender stereotypes are getting communicated through digital content.This content is majorly displayed as a text-and-image memes. With the growing prevalence of online content, it is essential to develop automated systems capable of detecting such harmful content to ensure safer online environments. This study focuses on the detection of misogynistic memes in two Dravidian languages, Tamil and Malayalam. The proposed model utilizes a pre-trained XLM-RoBERTa (XLM-R) model for text analysis and a Vision Transformer (ViT) for image feature extraction. A custom neural network classifier was trained on integrating the outputs of both modalities to form a unified representation. This model predicts whether the meme represents misogyny or not. This follows an early-fusion strategy since features of both modalities are combined before feeding into the classification model. This approach achieved promising results using a macro F1-score of 0.84066 on the Malayalam test dataset and 0.68830 on the Tamil test dataset. In addition, it is worth noting that this approach secured Rank 7 and 11 in Malayalam and Tamil classification respectively in the shared task of Misogyny Meme Detection (MMD). The findings demonstrate that the multimodal approach significantly enhances the accuracy of detecting misogynistic content compared to text-only or image-only models.
CUET_Novice@DravidianLangTech 2025: Abusive Comment Detection in Malayalam Text Targeting Women on Social Media Using Transformer-Based Models
Authors: Farjana Alam Tofa; Khadiza Sultana Sayma; Md Osama; Ashim Dey
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Social media has become a widely used platform for communication and entertainment, but it has also become a space where abuseand harassment can thrive. Women, in particular, face hateful and abusive comments that reflect gender inequality. This paper discussesour participation in the Abusive Text Targeting Women in Dravidian Languages shared task at DravidianLangTech@NAACL 2025, whichfocuses on detecting abusive text targeting women in Malayalam social media comments. The shared task provided a dataset of YouTubecomments in Tamil and Malayalam, focusing on sensitive and controversial topics where abusive behavior is prevalent. Our participationfocused on the Malayalam dataset, where the goal was to classify comments into these categories accurately. Malayalam-BERT achievedthe best performance on the subtask, securing 3rd place with a macro f1-score of 0.7083, highlighting the effectiveness of transformer models for low-resource languages. These results contribute to tackling gender-based abuse and improving online content moderation for underrepresented languages.
SemanticCuetSync@DravidianLangTech 2025: Multimodal Fusion for Hate Speech Detection - A Transformer Based Approach with Cross-Modal Attention
Authors: Md. Sajjad Hossain; Symom Hossain Shohan; Ashraful Islam Paran; Jawad Hossain; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: The rise of social media has significantly facilitated the rapid spread of hate speech. Detecting hate speech for content moderation is challenging, especially in low-resource languages (LRLs) like Telugu. Although some progress has been noticed in hate speech detection in Telegu concerning unimodal (text or image) in recent years, there is a lack of research on hate speech detection based on multimodal content detection (specifically using audio and text). In this regard, DravidianLangTech has arranged a shared task to address this challenge. This work explored three machine learning (ML), three deep learning (DL), and seven transformer-based models that integrate text and audio modalities using cross-modal attention for hate speech detection. The evaluation results demonstrate that mBERT achieved the highest F-1 score of 49.68% using text. However, the proposed multimodal attention-based approach with Whisper-small+TeluguBERT-3 achieved an F-1 score of 43 68%, which helped us achieve a rank of 3rd in the shared task competition.
CUET_Novice@DravidianLangTech 2025: A Bi-GRU Approach for Multiclass Political Sentiment Analysis of Tamil Twitter (X) Comments
Authors: Arupa Barua; Md Osama; Ashim Dey
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Political sentiment analysis in multilingual content poses significant challenges in capturing the subtle variations of diverse sentiments expressed in complex and low-resourced languages. Accurately classifying sentiments, whether positive, negative, or neutral, is crucialfor understanding public discourse. A shared task on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments, organized by DravidianLangTech@NAACL 2025, provided an opportunity to tackle these challenges. For this task, we implemented two data augmentation techniques, which are synonym replacement and back translation, and then explored various machine learning (ML) algorithms, including Logistic Regression, Decision Tree, Random Forest, SVM, and MultiNomial Naive Bayes. To capture the semantic meanings more efficiently, we experimented with deep learning (DL) models, including GRU, BiLSTM, BiGRU, and a hybrid CNN-BiLSTM.The Bidirectional Gated Recurrent Unit (BiGRU) achieved the best macro-F1 (MF1) score of 0.33, securing the 17th position in the sharedtask. These findings underscore the challenges of political sentiment analysis in low-resource languages and the need for advanced language-specific models for improved classification.
CIC-NLP@DravidianLangTech 2025: Detecting AI-generated Product Reviews in Dravidian Languages
Authors: Tewodros Achamaleh; Tolulope Olalekan Abiola; Lemlem Eyob Kawo; Mikiyas Mebraihtu; Grigori Sidorov
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: AI-generated text now matches human writing so well that telling them apart is very difficult. Our CIC-NLP team submits results for the DravidianLangTech@NAACL 2025 shared task to reveal AI-generated product reviews in Dravidian languages. We performed a binary classification task with XLM-RoBERTa-Base using the DravidianLangTech@NAACL 2025 datasets offered by the event organizers. Through training the model correctly, our tests could tell between human and AI-generated reviews with scores of 0.96 for Tamil and 0.88 for Malayalam in the evaluation test set. This paper presents detailed information about preprocessing, model architecture, hyperparameter fine-tuning settings, the experimental process, and the results. The source code is available on GitHub1.
One_by_zero@DravidianLangTech 2025: A Multimodal Approach for Misogyny Meme Detection in Malayalam Leveraging Visual and Textual Features
Authors: Dola Chakraborty; Shamima Afroz; Jawad Hossain; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Misogyny memes are a form of online content that spreads harmful and damaging ideas about women. By combining images and text, they often aim to mock, disrespect, or insult women, sometimes overtly and other times in more subtle, insidious ways. Detecting Misogyny memes is crucial for fostering safer and more respectful online communities. While extensive research has been conducted on high-resource languages (HRLs) like English, low-resource languages (LRLs) such as Dravidian (e.g., Tamil and Malayalam) remain largely overlooked. The shared task on Misogyny Meme Detection, organized as part of DravidianLangTech@NAACL 2025, provided a platform to tackle the challenge of identifying misogynistic content in memes, specifically in Malayalam. We participated in the competition and adopted a multimodal approach to contribute to this effort. For image analysis, we employed a ResNet18 model to extract visual features, while for text analysis, we utilized the IndicBERT model. Our system achieved an impressive F1-score of 0.87, earning us the 3rd rank in the task.
CUET-NLP_MP@DravidianLangTech 2025: A Transformer-Based Approach for Bridging Text and Vision in Misogyny Meme Detection in Dravidian Languages
Authors: Md. Mohiuddin; Md Minhazul Kabir; Kawsar Ahmed; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Misogyny memes, a form of digital content, reflect societal prejudices by discriminating against women through shaming and stereotyping. In this study, we present a multimodal approach combining Indic-BERT and ViT-base-patch16-224 to address misogyny memes. We explored various Machine Learning, Deep Learning, and Transformer models for unimodal and multimodal classification using provided Tamil and Malayalam meme dataset. Our findings highlight the challenges traditional ML and DL models face in understanding the nuances of Dravidian languages, while emphasizing the importance of transformer models in capturing these complexities. Our multimodal method achieved F1-scores of 77.18% and 84.11% in Tamil and Malayalam, respectively, securing 6th place for both languages among the participants.
CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Based Approach for Detecting AI-Generated Product Reviews in Low-Resource Dravidian Languages
Authors: Sabik Aftahee; Tofayel Ahmmed Babu; MD Musa Kalimullah Ratul; Jawad Hossain; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: E-commerce platforms face growing challenges regarding consumer trust and review authenticity because of the growing number of AI-generated product reviews. Low-resource languages (LRLs) such as Tamil and Malayalam face limited investigation by AI detection techniques because these languages experience constraints from sparse data sources and complex linguistic structures. The research team at CUET_NetworkSociety took part in the AI-Generated Review Detection contest during the DravidianLangTech@NAACL 2025 event to fill this knowledge void. Using a combination of machine learning, deep learning, and transformer-based models, we detected AI-generated and human-written reviews in both Tamil and Malayalam. The developed method employed DistilBERT, which underwent an advanced preprocessing pipeline and hyperparameter optimization using the Transformers library. This approach achieved a Macro F1-score of 0.81 for Tamil (Subtask 1), securing 18th place, and a score of 0.7287 for Malayalam (Subtask 2), ranking 25th.
CUET_NetworkSociety@DravidianLangTech 2025: A Multimodal Framework to Detect Misogyny Meme in Dravidian Languages
Authors: MD Musa Kalimullah Ratul; Sabik Aftahee; Tofayel Ahmmed Babu; Jawad Hossain; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Memes are commonly used for communication on social media platforms, and some of them can propagate misogynistic content, spreading harmful messages. Detecting such misogynistic memes has become a significant challenge, especially for low-resource languages like Tamil and Malayalam, due to their complex linguistic structures. To tackle this issue, a shared task on detecting misogynistic memes was organized at DravidianLangTech@NAACL 2025. This paper proposes a multimodal deep learning approach for detecting misogynistic memes in Tamil and Malayalam. The proposed model combines fine-tuned ResNet18 for visual feature extraction and indicBERT for analyzing textual content. The fused model was applied to the test dataset, achieving macro F1 scores of 76.32% for Tamil and 80.35% for Malayalam. Our approach led to 7th and 12th positions for Tamil and Malayalam, respectively.
CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Driven Approach to Political Sentiment Analysis of Tamil X (Twitter) Comments
Authors: Tofayel Ahmmed Babu; MD Musa Kalimullah Ratul; Sabik Aftahee; Jawad Hossain; Mohammed Moshiul Hoque
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: Social media has become an established medium of public communication and opinions on every aspect of life, but especially politics. This has resulted in a growing need for tools that can process the large amount of unstructured data that is produced on these platforms providing actionable insights in domains such as social trends and political opinion. Low-resource languages like Tamil present challenges due to limited tools and annotated data, highlighting the need for NLP focus on understudied languages. To address this, a shared task has been organized by DravidianLangTech@NAACL 2025 for political sentiment analysis for low-resource languages, with a specific focus on Tamil. In this task, we have explored several machine learning methods such as SVM, AdaBoost, GB, deep learning methods including CNN, LSTM, GRU BiLSTM, and the ensemble of different deep learning models, and transformer-based methods including mBERT, T5, XLM-R. The mBERT model performed best by achieving a macro F1 score of 0.2178 and placing our team 22nd in the rank list.
cantnlp@DravidianLangTech-2025: A Bag-of-Sounds Approach to Multimodal Hate Speech Detection
Authors: Sidney Wong; Andrew Li
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: This paper presents the systems and results for the Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL) shared task at the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech-2025). We took a ‘bag-of-sounds’ approach by training our hate speech detection system on the speech (audio) data using transformed Mel spectrogram measures. While our candidate model performed poorly on the test set, our approach offered promising results during training and development for Malayalam and Tamil. With sufficient and well-balanced training data, our results show that it is feasible to use both text and speech (audio) data in the development of multimodal hate speech detection systems.
LexiLogic@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian languages
Authors: Billodal Roy; Pranav Gupta; Souvik Bhattacharyya; Niranjan Kumar M
Venue: NAACL 2025 (Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages)

Abstract: This paper describes our participation in the DravidianLangTech@NAACL 2025 shared task on hate speech detection in Dravidian languages. While the task provided both text transcripts and audio data, we demonstrate that competitive results can be achieved using text features alone. We employed fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models from l3cube-pune for Malayalam, Tamil, and Telugu languages. Our system achieved notable results, securing second position for Tamil and Malayalam tasks, and first position for Telugu in the official leaderboard.