Due to technological advancements, various methods have emerged for disseminating news to the masses. The pervasive reach of news, however, has given rise to a significant concern: the proliferation of fake news. In response to this challenge, a shared task in Dravidian- LangTech EACL2024 was initiated to detect fake news and classify its types in the Malayalam language. The shared task consisted of two sub-tasks. Task 1 focused on a binary classification problem, determining whether a piece of news is fake or not. Whereas task 2 delved into a multi-class classification problem, categorizing news into five distinct levels. Our approach involved the exploration of various machine learning (RF, SVM, XGBoost, Ensemble), deep learning (BiLSTM, CNN), and transformer-based models (MuRIL, Indic- SBERT, m-BERT, XLM-R, Distil-BERT) by emphasizing parameter tuning to enhance overall model performance. As a result, we introduce a fine-tuned MuRIL model that leverages parameter tuning, achieving notable success with an F1-score of 0.86 in task 1 and 0.5191 in task 2. This successful implementation led to our system securing the 3rd position in task 1 and the 1st position in task 2. The source code will be found in the GitHub repository at this link: https://github.com/Salman1804102/ DravidianLangTech-EACL-2024-FakeNews.

With the continuous evolution of technology and widespread internet access, various social media platforms have gained immense popularity, attracting a vast number of active users globally. However, this surge in online activity has also led to a concerning trend by driving many individuals to resort to posting hateful and offensive comments or posts, publicly targeting groups or individuals. In response to these challenges, we participated in this shared task. Our approach involved proposing a fine-tuning-based pre-trained transformer model to effectively discern whether a given text contains offensive content that propagates hatred. We conducted comprehensive experiments, exploring various machine learning (LR, SVM, and Ensemble), deep learning (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-SBERT, m- BERT, MuRIL, Distil-BERT, XLM-R), adhering to a meticulous fine-tuning methodology. Among the models evaluated, our fine-tuned L3Cube-Indic-Sentence-Similarity- BERT or Indic-SBERT model demonstrated superior performance, achieving a macro-average F1-score of 0.7013. This notable result positioned us at the 6th place in the task. The implementation details of the task will be found in the GitHub repository.

Textual Sentiment Analysis (TSA) delves into people’s opinions, intuitions, and emotions regarding any entity. Natural Language Processing (NLP) serves as a technique to extract subjective knowledge, determining whether an idea or comment leans positive, negative, neutral, or a mix thereof toward an entity. In recent years, it has garnered substantial attention from NLP researchers due to the vast availability of online comments and opinions. Despite extensive studies in this domain, sentiment analysis in low-resourced languages such as Tamil and Tulu needs help handling code-mixed and transliterated content. To address these challenges, this work focuses on sentiment analysis of code-mixed and transliterated Tamil and Tulu social media comments. It explored four machine learning (ML) approaches (LR, SVM, XGBoost, Ensemble), four deep learning (DL) methods (BiLSTM and CNN with FastText and Word2Vec), and four transformer-based models (m-BERT, MuRIL, L3Cube-IndicSBERT, and Distilm-BERT) for both languages. For Tamil, L3Cube-IndicSBERT and ensemble approaches outperformed others, while m-BERT demonstrated superior performance among the models for Tulu. The presented models achieved the 3^rd and 1^st ranks by attaining macro F1-scores of 0.227 and 0.584 in Tamil and Tulu, respectively.