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.