The Faux-Hate task looks at two big issues:misinformation and hate speech. It focuses onHindi-English social media posts. This papershares our methods for both parts of the task.For Task A, we built a special model based onXLM-RoBERTa. It has features that help usspot both fake news and hate speech at the sametime. For Task B, we wanted to identify whothe hate is aimed at (like individuals or groups)and how severe it is (high, medium, low). So,we added different tools to our model for thiskind of sorting. To get ready for all this, wecarefully cleaned the data, especially dealingwith mixing languages and different spellings.In Task A, our results show that our model canclearly tell the difference between fake and realstories, as well as between hate and non-hatespeech. For Task B, it does a good job withidentifying targets and severity levels, givingstrong predictions for multiple labels. Overall,these results show that cross-lingual models,combined with specific tweaks, can really helptackle complex text classification in languageswith fewer resources.

The prevalence of harmful internet content is on the rise, especially among young people. Thismakes social media sites breeding grounds forhate speech and negativity even though theirpurpose is to create connections. The study pro-poses a multi-task learning model for the iden-tification and analysis of harmful social mediacontent. This classifies the text into fake/realand hate/non-hate categories and further identi-fies the target and severity of the harmful con-tent. The proposed model showed significantimprovements in performance with training ontransliterated data as compared to code-mixeddata. It ranked 2nd and 3rd in the ICON 2024Faux-Hate Shared Task and the performanceshave made it very effective against harmful con-tent.