Online product reviews influence customer choices and company reputations. However, companies can counter negative reviews by generating fake reviews that portray their products positively. These fake reviews lead to legal disputes and concerns, particularly because AI detection tools are limited in low-resource languages such as Tamil and Malayalam. To address this, we use machine learning and deep learning techniques to identify AI-generated reviews. We utilize Tamil BERT and Malayalam BERT in the embedding layer to extract contextual features. These features are sent to a Feedforward Neural Network (FFN) with softmax to classify reviews as AI-generated or not. The performance of the model is evaluated on the dataset. The results show that the transformer-based embedding achieves a better accuracy of 91.98% on Tamil data and an accuracy of 87.50% on Malayalam data.

The detection of AI-generated product reviews is critical due to the increased use of large language models (LLMs) and their capability to generate convincing sentences. The AI-generated reviews can affect the consumers and businesses as they influence the trust and decision-making. This paper presents the overview of the shared task on Detecting AI-generated product reviews in Dravidian Languages” organized as part of DravidianLangTech@NAACL 2025. This task involves two subtasks—one in Malayalam and another in Tamil, both of which are binary classifications where a review is to be classified as human-generated or AI-generated. The dataset was curated by collecting comments from YouTube videos. Various machine learning and deep learning-based models ranging from SVM to transformer-based architectures were employed by the participants.

Sentiment analysis, the task of identifying subjective opinions or emotional responses, has become increasingly significant with the rise of social media. However, analysing sentiment in Dravidian languages such as Tamil-English and Tulu-English presents unique challenges due to linguistic code-switching (where people tend to mix multiple languages) and non-native scripts. Traditional monolingual sentiment analysis models struggle to address these complexities effectively. This research explores a fine-tuned transformer model based on the XLM-RoBERTa model for sentiment detection. It utilizes the tokenizer from the XLM-RoBERTa model for text preprocessing. Additionally, the performance of the XLM-RoBERTa model was compared with traditional machine learning models such as Logistic Regression (LR) and Random Forest (RF), as well as other transformer-based models like BERT and RoBERTa. This research was based on our work for the Sentiment Analysis in Tamil and Tulu DravidianLangTech@NAACL 2025 competition, where we received a macro F1-score of 59% for the Tulu dataset and 49% for the Tamil dataset, placing third in the competition.

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.

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 “Fake News Detection in Dravidian Languages—DravidianLangTech@NAACL 2025.” 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, Naïve 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%.

The rise of online social media has revolutionized communication, offering users a convenient way to share information and stay updated on current events. However, this surge in connectivity has also led to the proliferation of misinformation, commonly known as fake news. This misleading content, often disguised as legitimate news, poses a significant challenge as it can distort public perception and erode trust in reliable sources. This shared task consists of two subtasks such as task 1 and task 2. Task 1 aims to classify a given social media text into original or fake. The goal of the FakeDetect-Malayalam task2 is to encourage participants to develop effective models capable of accurately detecting and classifying fake news articles in the Malayalam language into different categories like False, Half True, Mostly False, Partly False, and Mostly True. For this shared task, 33 participants submitted their results.

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