ymoslem/acl-abstracts huggingface dataset:
CIC-NLP@DravidianLangTech 2025: Fake News Detection in Dravidian Languages (2025)

Misinformation is a growing problem for technologycompanies and for society. Although there exists a large body of related work on identifying fake news in predominantlyresource languages, there is unfortunately a lack of such studies in low-resource languages (LRLs). Because corpora and annotated data are scarce in LRLs, the identification of false information remains at an exploratory stage. Fake news detection is critical in this digital era to avoid spreading misleading information. This research work presents an approach to Detect Fake News in Dravidian Languages. Our team CIC-NLP work primarily targets Task 1 which involves identifying whether a given social platform news is original or fake. For fake news detection (FND) problem, we used mBERT model and utilized the dataset that was provided by the organizers of the workshop. In this work, we describe our findings and the results of the proposed method. Our mBERT model achieved an F1 score of 0.853.
CoreFour\_IIITK@DravidianLangTech 2025: Abusive Content Detection Against Women Using Machine Learning And Deep Learning Models (2025)

The rise in utilizing social media platforms increased user-generated content significantly, including negative comments about women in Tamil and Malayalam. While these platforms encourage communication and engagement, they also become a medium for the spread of abusive language, which poses challenges to maintaining a safe online environment for women. Prevention of usage of abusive content against women as much as possible is the main issue focused in the research. This research focuses on detecting abusive language against women in Tamil and Malayalam social media comments using computational models, such as Logistic regression model, Support vector machines (SVM) model, Random forest model, multilingual BERT model, XLM-Roberta model, and IndicBERT. These models were trained and tested on a specifically curated dataset containing labeled comments in both languages. Among all the approaches, IndicBERT achieved a highest macro F1-score of 0.75. The findings emphasize the significance of employing a combination of traditional and advanced computational techniques to address challenges in Abusive Content Detection (ACD) specific to regional languages.
The\_Deathly\_Hallows@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages (2025)

The DravidianLangTech@NAACL 2025 shared task focused on multimodal hate speech detection in Tamil, Telugu, and Malayalam using social media text and audio. Our approach integrated advanced preprocessing, feature extraction, and deep learning models. For text, preprocessing steps included normalization, tokenization, stopword removal, and data augmentation. Feature extraction was performed using TF-IDF, Count Vectorizer, BERT-base-multilingual-cased, XLM-Roberta-Base, and XLM-Roberta-Large, with the latter achieving the best performance. The models attained training accuracies of 83{\%} (Tamil), 88{\%} (Telugu), and 85{\%} (Malayalam). For audio, Mel Frequency Cepstral Coefficients (MFCCs) were extracted and enhanced with augmentation techniques such as noise addition, time-stretching, and pitch-shifting. A CNN-based model achieved training accuracies of 88{\%} (Tamil), 88{\%} (Telugu), and 93{\%} (Malayalam). Macro F1 scores ranked Tamil 3rd (0.6438), Telugu 15th (0.1559), and Malayalam 12th (0.3016). Our study highlights the effectiveness of text-audio fusion in hate speech detection and underscores the importance of preprocessing, multimodal techniques, and feature augmentation in addressing hate speech on social media.
SSN\_IT\_NLP@DravidianLangTech 2025: Abusive Tamil and Malayalam Text targeting Women on Social Media (2025)

The proliferation of social media platforms has resulted in increased instances of online abuse, particularly targeting marginalized groups such as women. This study focuses on the classification of abusive comments in Tamil and Malayalam, two Dravidian languages widely spoken in South India. Leveraging a multilingual BERT model, this paper provides an effective approach for detecting and categorizing abusive and non-abusive text. Using labeled datasets comprising social media comments, our model demonstrates its ability to identify targeted abuse with promising accuracy. This paper outlines the dataset preparation, model architecture, training methodology, and the evaluation of results, providing a foundation for combating online abuse in low-resource languages.The methodology is unique for its integration of multilingual BERT and weighted loss functions to address class imbalance, showcasing a pathway for effective abuse detection in other underrepresented languages. The BERT model achieved an F1-score of 0.6519 for Tamil and 0.6601 for Malayalam. The codefor this work is available on github Abusive-Text-targeting-women
Findings of the Shared Task on Abusive Tamil and Malayalam Text Targeting Women on Social Media: DravidianLangTech@NAACL 2025 (2025)

This overview paper presents the findings of the Shared Task on Abusive Tamil and Malayalam Text Targeting Women on Social Media, organized as part of DravidianLangTech@NAACL 2025. The task aimed to encourage the development of robust systems to detectabusive content targeting women in Tamil and Malayalam, two low-resource Dravidian languages. Participants were provided with annotated datasets containing abusive and nonabusive text curated from YouTube comments. We present an overview of the approaches and analyse the results of the shared task submissions. We believe the findings presented in this paper will be useful to researchers working in Dravidian language technology.
Celestia@DravidianLangTech 2025: Malayalam-BERT and m-BERT based transformer models for Fake News Detection in Dravidian Languages (2025)

Fake news detection in Malayalam is difficult due to limited data and language challenges. This study compares machine learning, deep learning, and transformer models for classification. The dataset is balanced and divided into training, development and test sets. Machine learning models (SVM, Random Forest, Naive Bayes) used TF-IDF features and deep learning models (LSTM, BiLSTM, CNN) worked with tokenized sequences. We fine-tuned transformer models like IndicBERT, MuRIL, mBERT, and Malayalam-Bert. Among them, the Malayalam-Bert model performed the best and achieved an F1 score of 86{\%}. On the other hand mBERT performed best at spotting fake news. However, the models struggled with mixed-language text and complex writing. Despite these challenges, transformer models turned out to be the most effective for detecting fake news in Malayalam.
DravLingua@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages using Late Fusion of Muril and Wav2Vec Models (2025)

Detecting hate speech on social media is increasingly difficult, particularly in low-resource Dravidian languages such as Tamil, Telugu and Malayalam. Traditional approaches primarily rely on text-based classification, often overlooking the multimodal nature of online communication, where speech plays a pivotal role in spreading hate speech. We propose a multimodal hate speech detection model using a late fusion technique that integrates Wav2Vec 2.0 for speech processing and Muril for text analysis. Our model is evaluated on the DravidianLangTech@NAACL 2025 dataset, which contains speech and text data in Telugu, Tamil, and Malayalam scripts. The dataset is categorized into six classes: Non-Hate, Gender Hate, Political Hate, Religious Hate, Religious Defamation, and Personal Defamation. To address class imbalance, we incorporate class weighting and data augmentation techniques. Experimental results demonstrate that the late fusion approach effectively captures patterns of hate speech that may be missed when analyzing a single modality. This highlights the importance of multimodal strategies in enhancing hate speech detection, particularly for low-resource languages.
Trio Innovators @ DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages (2025)

This paper presents an in-depth study on multimodal hate speech detection in Dravidian languages{---}Tamil, Telugu, and Malayalam{---}by leveraging both audio and text modalities. Detecting hate speech in these languages is particularly challenging due to factors such as codemixing, limited linguistic resources, and diverse cultural contexts. Our approach integrates advanced techniques for audio feature extraction and XLM-Roberta for text representation, with feature alignment and fusion to develop a robust multimodal framework. The dataset is carefully categorized into labeled classes: gender-based, political, religious, and personal defamation hate speech, along with a non-hate category. Experimental results indicate that our model achieves a macro F1-score of 0.76 and an accuracy of approximately 85.
Wictory@DravidianLangTech 2025: Political Sentiment Analysis of Tamil X(Twitter) Comments using LaBSE and SVM (2025)

Political sentiment analysis has become an essential area of research in Natural Language Processing (NLP), driven by the rapid rise ofsocial media as a key platform for political discourse. This study focuses on sentiment classification in Tamil political tweets, addressing the linguistic and cultural complexities inherent in low-resource languages. To overcome data scarcity challenges, we develop a system that integrates embeddings with advanced Machine Learning techniques, ensuring effective sentiment categorization. Our approach leverages deep learning-based models and transformer architectures to capture nuanced expressions, contributing to improved sentiment classification. This work enhances NLP methodologies for low-resource languages and provides valuable insights into Tamil political discussions, aiding policymakers and researchers in understanding public sentiment more accurately. Notably, our system secured $Rank 5$in the NAACL shared task, demonstrating its effectiveness in real-world sentiment classification challenges.
ANSR@DravidianLangTech 2025: Detection of Abusive Tamil and Malayalam Text Targeting Women on Social Media using RoBERTa and XGBoost (2025)

Abusive language directed at women on social media, often characterized by crude slang, offensive terms, and profanity, is not just harmful communication but also acts as a tool for serious and widespread cyber violence. It is imperative that this pressing issue be addressed in order to establish safer online spaces and provide efficient methods for detecting and minimising this kind of abuse. However, the intentional masking of abusive language, especially in regional languages like Tamil and Malayalam, presents significant obstacles, making detection and prevention more difficult. The system created effectively identifies abusive sentences using supervised machine learning techniques based on RoBerta embeddings. The method aims to improve upon the current abusive language detection systems, which are essential for various online platforms, including social media and online gaming services. The proposed method currently ranked 8 in malayalam and 20 in tamil in terms of f1 score.
Synapse@DravidianLangTech 2025: Multiclass Political Sentiment Analysis in Tamil X (Twitter) Comments: Leveraging Feature Fusion of IndicBERTv2 and Lexical Representations (2025)

Social media platforms like X (twitter) have gained popularity for political debates and election campaigns in the last decade. This creates the need to moderate and understand the sentiments of the tweets in order to understand the state of digital campaigns. This paper focuses on political sentiment classification of Tamil X (Twitter) comments which proves to be challenging because of the presence of informal expressions, code-switching, and limited annotated datasets. This study focuses on categorizing them into seven classes: substantiated, sarcastic, opinionated, positive, negative, neutral, and none of the above. This paper proposes a solution to Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments - DravidianLangTech@NAACL 2025 shared task, the solution incorporates IndicBERTv2-MLM-Back-Translation model and TF-IDF vectors into a custom model. Further we explore the use of preprocessing techniques to enrich hashtags and emojis with their context. Our approach achieved Rank 1 with a macro F1 average of 0.38 in the shared task.
Findings of the Shared Task on Misogyny Meme Detection: DravidianLangTech@NAACL 2025 (2025)

The rapid expansion of social media has facilitated communication but also enabled the spread of misogynistic memes, reinforcing gender stereotypes and toxic online environments. Detecting such content is challenging due to the multimodal nature of memes, where meaning emerges from the interplay of text and images. The Misogyny Meme Detection shared task at DravidianLangTech@NAACL 2025 focused on Tamil and Malayalam, encouraging the development of multimodal approaches. With 114 teams registered and 23 submitting predictions, participants leveraged various pretrained language models and vision models through fusion techniques. The best models achieved high macro F1 scores (0.83682 for Tamil, 0.87631 for Malayalam), highlighting the effectiveness of multimodal learning. Despite these advances, challenges such as bias in the data set, class imbalance, and cultural variations persist. Future research should refine multimodal detection methods to improve accuracy and adaptability, fostering safer and more inclusive online spaces.
Overview of the Shared Task on Sentiment Analysis in Tamil and Tulu (2025)

Sentiment analysis is an essential task for interpreting subjective opinions and emotions in textual data, with significant implications across commercial and societal applications. This paper provides an overview of the shared task on Sentiment Analysis in Tamil and Tulu, organized as part of DravidianLangTech@NAACL 2025. The task comprises two components: one addressing Tamil and the other focusing on Tulu, both designed as multi-class classification challenges, wherein the sentiment of a given text must be categorized as positive, negative, neutral and unknown. The dataset was diligently organized by aggregating user-generated content from social media platforms such as YouTube and Twitter, ensuring linguistic diversity and real-world applicability. Participants applied a variety of computational approaches, ranging from classical machine learning algorithms such as Traditional Machine Learning Models, Deep Learning Models, Pre-trained Language Models and other Feature Representation Techniques to tackle the challenges posed by linguistic code-mixing, orthographic variations, and resource scarcity in these low resource languages.
cuetRaptors@DravidianLangTech 2025: Transformer-Based Approaches for Detecting Abusive Tamil Text Targeting Women on Social Media (2025)

With the exponential growth of social media usage, the prevalence of abusive language targeting women has become a pressing issue, particularly in low-resource languages (LRLs) like Tamil and Malayalam. This study is part of the shared task at DravidianLangTech@NAACL 2025, which focuses on detecting abusive comments in Tamil social media content. The provided dataset consists of binary-labeled comments (Abusive or Non-Abusive), gathered from YouTube, reflecting explicit abuse, implicit bias, stereotypes, and coded language. We developed and evaluated multiple models for this task, including traditional machine learning algorithms (Logistic Regression, Support Vector Machine, Random Forest Classifier, and Multinomial Naive Bayes), deep learning models (CNN, BiLSTM, and CNN+BiLSTM), and transformer-based architectures (DistilBERT, Multilingual BERT, XLM-RoBERTa), and fine-tuned variants of these models. Our best-performing model, Multilingual BERT, achieved a weighted F1-score of 0.7203, ranking 19 in the competition.
Overview on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments: DravidianLangTech@NAACL 2025 (2025)

Political multiclass detection is the task of identifying the predefined seven political classes. In this paper, we report an overview of the findings on the {\textquotedblleft}Political Multiclass Sentiment Analysis of Tamil X(Twitter) Comments{\textquotedblright} shared task conducted at the workshop on DravidianLangTech@NAACL 2025. The participants were provided with annotated Twitter comments, which are split into training, development, and unlabelled test datasets. A total of 139 participants registered for this shared task, and 25 teams finally submitted their results. The performance of the submitted systems was evaluated and ranked in terms of the macro-F1 score.
KEC\_AI\_BRIGHTRED@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian languages (2025)

Hate speech detection in multilingual settings presents significant challenges due to linguistic variations and speech patterns across different languages. This study proposes a fusion-based approach that integrates audio and text features to enhance classification accuracy in Tamil, Telugu, and Malayalam. We extract Mel- Frequency Cepstral Coefficients and their delta variations for speech representation, while textbased features contribute additional linguistic insights. Several models were evaluated, including BiLSTM, Capsule Networks with Attention, Capsule-GRU, ConvLSTM-BiLSTM, and Multinomial Na{\"i}ve Bayes, to determine the most effective architecture. Experimental results demonstrate that Random Forest performs best for text classification, while CNN achieves the highest accuracy for audio classification. The model was evaluated using the Macro F1 score and ranked ninth in Tamil with a score of 0.3018, ninth in Telugu with a score of 0.251, and thirteenth in Malayalam with a score of 0.2782 in the Multimodal Social Media Data Analysis in Dravidian Languages shared task at DravidianLangTech@NAACL 2025. By leveraging feature fusion and optimized model selection, this approach provides a scalable and effective framework for multilingual hate speech detection, contributing to improved content moderation on social media platforms.
Overview of the Shared Task on Fake News Detection in Dravidian Languages-DravidianLangTech@NAACL 2025 (2025)

Detecting and mitigating fake news on social media is critical for preventing misinformation, protecting democratic processes, preventing public distress, mitigating hate speech, reducing financial fraud, maintaining information reliability, etc. This paper summarizes the findings of the shared task {\textquotedblleft}Fake News Detection in Dravidian Languages{---}DravidianLangTech@NAACL 2025.{\textquotedblright} The goal of this task is to detect fake content in social media posts in Malayalam. It consists of two subtasks: the first focuses on binary classification (Fake or Original), while the second categorizes the fake news into five types{---}False, Half True, Mostly False, Partly False, and Mostly True. In Task 1, 22 teams submitted machine learning techniques like SVM, Na{\"i}ve Bayes, and SGD, as well as BERT-based architectures. Among these, XLM-RoBERTa had the highest macro F1 score of 89.8{\%}. For Task 2, 11 teams submitted models using LSTM, GRU, XLM-RoBERTa, and SVM. XLM-RoBERTa once again outperformed other models, attaining the highest macro F1 score of 68.2{\%}.
Is a bunch of words enough to detect disagreement in hateful content? (2025)

The complexity of the annotation process when adopting crowdsourcing platforms for labeling hateful content can be linked to the presence of textual constituents that can be ambiguous, misinterpreted, or characterized by a reduced surrounding context. In this paper, we address the problem of perspectivism in hateful speech by leveraging contextualized embedding representation of their constituents and weighted probability functions. The effectiveness of the proposed approach is assessed using four datasets provided for the SemEval 2023 Task 11 shared task. The results emphasize that a few elements can serve as a proxy to identify sentences that may be perceived differently by multiple readers, without the need of necessarily exploiting complex Large Language Models.
On Crowdsourcing Task Design for Discourse Relation Annotation (2025)

Interpreting implicit discourse relations involves complex reasoning, requiring the integration of semantic cues with background knowledge, as overt connectives like {\textquotedblleft}because{\textquotedblright} or {\textquotedblleft}then{\textquotedblright} are absent. These relations often allow multiple interpretations, best represented as distributions. In this study, we compare two established methods that crowdsource implicit discourse relation annotation by connective insertion: a free-choice approach, which allows annotators to select any suitable connective, and a forced-choice approach, which asks them to select among a set of predefined options. Specifically, we re-annotate the whole DiscoGeM 1.0 corpus - initially annotated with the free-choice method - using the forced-choice approach. The free-choice approach allows for flexible and intuitive insertion of various connectives, which are context-dependent. Comparison among over 130,000 annotations, however, shows that the free-choice strategy produces less diverse annotations, often converging on common labels. Analysis of the results reveals the interplay between task design and the annotators' abilities to interpret and produce discourse relations.
Sources of Disagreement in Data for LLM Instruction Tuning (2025)

In this paper we study the patterns of label disagreement in data used for instruction tuning Large Language models (LLMs). Specifically, we focus on data used for Reinforcement Learning from Human Feedback (RLHF). Our objective is to determine what is the primary source of disagreement: the individual data points, the choice of annotators, or the task formulation. We annotate the same dataset multiple times under different conditions and compare the overall agreement and the patterns of disagreement. For task formulation, we compare {\textquotedblleft}single{\textquotedblright} format where annotators rate LLM responses individually with {\textquotedblleft}preference{\textquotedblright} format where annotators select one of two possible responses. For annotators, we compare data from human labelers with automatic data labeling using LLMs. Our results indicate that: (1) there are very few {\textquotedblleft}universally ambiguous{\textquotedblright} instances. The label disagreement depends largely on the task formulation and the choice of annotators; (2) the overall agreement remains consistent across experiments. We find no evidence that {\textquotedblleft}preference{\textquotedblright} data is of higher quality than {\textquotedblleft}single{\textquotedblright} data; and (3) the change of task formulation and annotators impacts the resulting instance-level labels. The labels obtained in different experiments are correlated, but not identical.
CoMeDi Shared Task: Median Judgment Classification \& Mean Disagreement Ranking with Ordinal Word-in-Context Judgments (2025)

We asked task participants to solve two subtasks given a pair of word usages: Ordinal Graded Word-in-Context Classification (OGWiC) and Disagreement in Word-in-Context Ranking (DisWiC). The tasks take a different view on modeling of word meaning by (i) treating WiC as an ordinal classification task, and (ii) making disagreement the explicit detection aim (instead of removing it). OGWiC is solved with relatively high performance while DisWiC proves to be a challenging task. In both tasks, the dominating model architecture uses independently optimized binary Word-in-Context models.