Fact-checking of health-related claims has become necessary in this digital age, where any information posted online is easily available to everyone. The most effective way to verify such claims is by using evidences obtained from reliable sources of medical knowledge, such as PubMed. Recent advances in the field of NLP have helped automate such fact-checking tasks. In this work, we propose a domain-specific BERT-based model using a transfer learning approach for the task of predicting the veracity of claim-evidence pairs for the verification of health-related facts. We also improvise on a method to combine multiple evidences retrieved for a single claim, taking into consideration conflicting evidences as well. We also show how our model can be exploited when labelled data is available and how back-translation can be used to augment data when there is data scarcity.

This work explores Arabic disinformation identification, a crucial task in natural language processing, using a state-of-the-art NLP model. We highlight the performance of our system model against baseline models, including multilingual and Arabic-specific ones, and showcase the effectiveness of domain-specific pre-trained models. This work advocates for the adoption of tailored pre-trained models in NLP, emphasizing their significance in understanding diverse languages. By merging advanced NLP techniques with domain-specific pre-training, it advances Arabic disinformation identification.

Language identification has recently gained research interest in code-mixed languages due to the extensive use of social media among people. People who speak multiple languages tend to use code-mixed languages when communicating with each other. It has become necessary to identify the languages in such code-mixed environment to detect hate speeches, fake news, misinformation or disinformation and for tasks such as sentiment analysis. In this work, we have proposed a BERT-based approach for language identification in the CoLI-Kanglish shared task at ICON 2022. Our approach achieved 86% weighted average F-1 score and a macro average F-1 score of 57% in the test set.