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.

Machine-written texts are gradually becoming indistinguishable from human-generated texts, leading to the need to use sophisticated methods to detect them. Team CIC-NLP presents work in the Gen-AI Content Detection Task 1 at COLING 2025 Workshop: the focus of our work is on Subtask B of Task 1, which is the classification of text written by machines and human authors, with particular attention paid to identifying multilingual binary classification problem. Usng mBERT, we addressed the binary classification task using the dataset provided by the GenAI Detection Task team. mBERT acchieved a macro-average F1-score of 0.72 as well as an accuracy score of 0.73.

Social media platforms and user-generated content, such as tweets, comments, and blog posts often contain offensive language, including racial hate speech, personal attacks, and sexual harassment. Detecting such inappropriate language is essential to ensure user safety and to prevent the spread of hateful behavior and online aggression. Approaches base on conventional machine learning and deep learning have shown robust results for high-resource languages like English and find it hard to deal with code-mixed text, which is common in bilingual communication. We participated in the shared task “LT-EDI@LDK 2025” organized by DravidianLangTech, applying the BERT-base multilingual cased model and achieving an F1 score of 0.63. These results demonstrate how our model effectively processes and interprets the unique linguistic features of code-mixed content. The source code is available on GitHub.1

Emotions play a fundamental role in the decision-making process, shaping human actions across diverse disciplines. The extensive usage of emotion intensity detection approaches has generated substantial research interest during the last few years. Efficient multi-label emotion intensity detection remains unsatisfactory even for high-resource languages, with a substantial performance gap among well-resourced and under-resourced languages. Team {textbf{Tewodros}} participated in SemEval-2025 Task 11, Track B, focusing on detecting text-based emotion intensity. Our work involved multi-label emotion intensity detection across three languages: Amharic, English, and Spanish, using the (afro-xlmr-large-76L), (DeBERTa-v3-base), and (BERT-base-Spanish-wwm-uncased) models. The models achieved an average F1 score of 0.6503 for Amharic, 0.5943 for English, and an accuracy score of 0.6228 for Spanish. These results demonstrate the effectiveness of our models in capturing emotion intensity across multiple languages.