The detection of hate speech in social media platforms is very crucial these days. This is due to its adverse impact on mental health, social harmony, and online safety. This paper presents the overview of the shared task on Multimodal Hate Speech Detection in Dravidian Languages organized as part of DravidianLangTech@NAACL 2025. The task emphasizes detecting hate speech in social media content that combines speech and text. Here, we focus on three low-resource Dravidian languages: Malayalam, Tamil, and Telugu. Participants were required to classify hate speech in three sub-tasks, each corresponding to one of these languages. The dataset was curated by collecting speech and corresponding text from YouTube videos. Various machine learning and deep learning-based models, including transformer-based architectures and multimodal frameworks, were employed by the participants. The submissions were evaluated using the macro F1 score. Experimental results underline the potential of multimodal approaches in advancing hate speech detection for low-resource languages. Team SSNTrio achieved the highest F1 score in Malayalam and Tamil of 0.7511 and 0.7332, respectively. Team lowes scored the best F1 score of 0.3817 in the Telugu sub-task.

Social media is a powerful communication tooland rich in diverse content requiring innovativeapproaches to understand nuances of the lan-guages. Addressing challenges like hate speechnecessitates multimodal analysis that integratestextual, and other cues to capture its contextand intent effectively. This paper proposes amultimodal hate speech detection system inTamil, which uses textual and audio featuresfor classification. Our proposed system usesa fine-tuned Indic-BERT model for text basedhate speech detection and Wav2Vec2 modelfor audio based hate speech detection of au-dio data. The fine-tuned Indic-BERT modelwith Whisper achieved an F1 score of 0.25 onMultimodal approach. Our proposed approachranked at 10th position in the shared task onMultimodal Hate Speech Detection in Dravid-ian languages at the NAACL 2025 WorkshopDravidianLangTech.

Abusive language toward women on the Internet has always been perceived as a danger to free speech and safe online spaces. In this paper, we discuss three transformer-based models - BERT, XLM-RoBERTa, and DistilBERT-in identifying gender-abusive comments in Tamil and Malayalam YouTube contents. We fine-tune and compare these models using a dataset provided by DravidianLangTech 2025 shared task for identifying the abusive content from social media. Compared to the models above, the results of XLM-RoBERTa are better and reached F1 scores of 0.7708 for Tamil and 0.6876 for Malayalam. BERT followed with scores of 0.7658 (Tamil) and 0.6671 (Malayalam). Of the DistilBERTs, performance was varyingly different for the different languages. A large difference in performance between the models, especially in the case of Malayalam, indicates that working in low-resource languages is difficult. The choice of a model is extremely critical in applying abusive language detection. The findings would be important information for effective content moderation systems in linguistically diverse contexts. In general, it would promote safe online spaces for women in South Indian language communities.

Effectively managing offensive content is crucial on social media platforms to encourage positive online interactions. However, addressing offensive contents in code-mixed Dravidian languages faces challenges, as current moderation methods focus on flagging entire comments rather than pinpointing specific offensive segments. This limitation stems from a lack of annotated data and accessible systems designed to identify offensive language sections. To address this, our shared task presents a dataset comprising Kannada-English code-mixed social comments, encompassing offensive comments. This paper outlines the dataset, the utilized algorithms, and the results obtained by systems participating in this shared task.

Detecting hate speech in code-mixed language is vital for a secure online space, curbing harmful content, promoting inclusive communication, and safeguarding users from discrimination. Despite the linguistic complexities of code-mixed languages, this study explores diverse pre-processing methods. It finds that the Transliteration method excels in handling linguistic variations. The research comprehensively investigates machine learning and deep learning approaches, namely Logistic Regression and Bi-directional Gated Recurrent Unit (Bi-GRU) models. These models achieved F1 scores of 0.68 and 0.70, respectively, contributing to ongoing efforts to combat hate speech in code-mixed languages and offering valuable insights for future research in this critical domain.

Online Social Network has let people to connect and interact with each other. It does, however, also provide a platform for online abusers to propagate abusive content. The vast majority of abusive remarks are written in a multilingual style, which allows them to easily slip past internet inspection. This paper presents a system developed for the Shared Task on Abusive Comment Detection (Misogyny, Misandry, Homophobia, Transphobic, Xenophobia, CounterSpeech, Hope Speech) in Tamil DravidianLangTech@ACL 2022 to detect the abusive category of each comment. We approach the task with three methodologies - Machine Learning, Deep Learning and Transformer-based modeling, for two sets of data - Tamil and Tamil+English language dataset. The dataset used in our system can be accessed from the competition on CodaLab. For Machine Learning, eight algorithms were implemented, among which Random Forest gave the best result with Tamil+English dataset, with a weighted average F1-score of 0.78. For Deep Learning, Bi-Directional LSTM gave best result with pre-trained word embeddings. In Transformer-based modeling, we used IndicBERT and mBERT with fine-tuning, among which mBERT gave the best result for Tamil dataset with a weighted average F1-score of 0.7.

Identifying offensive speech is an exciting andessential area of research, with ample tractionin recent times. This paper presents our sys-tem submission to the subtask 1, focusing onusing supervised approaches for extracting Of-fensive spans from code-mixed Tamil-Englishcomments. To identify offensive spans, wedeveloped the Bidirectional Long Short-TermMemory (BiLSTM) model with Glove Em-bedding. To this end, the developed systemachieved an overall F1 of 0.1728. Addition-ally, for comments with less than 30 characters,the developed system shows an F1 of 0.3890,competitive with other submissions.

Offensive content moderation is vital in social media platforms to support healthy online discussions. However, their prevalence in code-mixed Dravidian languages is limited to classifying whole comments without identifying part of it contributing to offensiveness. Such limitation is primarily due to the lack of annotated data for offensive spans. Accordingly, in this shared task, we provide Tamil-English code-mixed social comments with offensive spans. This paper outlines the dataset so released, methods, and results of the submitted systems.

Depression is linked to the development of dementia. Cognitive functions such as thinkingand remembering generally deteriorate in dementiapatients. Social media usage has beenincreased among the people in recent days. Thetechnology advancements help the communityto express their views publicly. Analysing thesigns of depression from texts has become animportant area of research now, as it helps toidentify this kind of mental disorders among thepeople from their social media posts. As part ofthe shared task on detecting signs of depressionfrom social media text, a dataset has been providedby the organizers (Sampath et al.). Weapplied different machine learning techniquessuch as Support Vector Machine, Random Forestand XGBoost classifier to classify the signsof depression. Experimental results revealedthat, the XGBoost model outperformed othermodels with the highest classification accuracyof 0.61% and an Macro F1 score of 0.54.

Memes became an important way of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.

of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.

Internet advancements have made a huge impact on the communication pattern of people and their life style. People express their opinion on products, politics, movies etc. in social media. Even though, English is predominantly used, nowadays many people prefer to tweet in their native language and some- times by combining it with English. Sentiment analysis on such code-mixed tweets is challenging, due to large vocabulary, grammar and colloquial usage of many words. In this paper, the transformer based language model is applied to analyse the sentiment on Tanglish tweets, which is a combination of Tamil and English. This work has been submitted to the the shared task on DravidianLangTech- EACL2021. From the experimental results, it is shown that an F 1 score of 64% was achieved in detecting the hate speech in code-mixed Tamil-English tweets using bidirectional trans- former model.