In recent years, the rise of harmful narratives online has highlighted the need for advancedhate speech detection models. One emergingchallenge is the phenomenon of Faux Hate, anew type of hate speech that originates fromthe intersection of fake narratives and hatespeech. Faux Hate occurs when fabricatedclaims fuel the generation of hateful language,often blurring the line between misinforma-tion and malicious intent. Identifying suchspeech becomes especially difficult when thefake claim itself is not immediately apparent.This paper provides an overview of a sharedtask competition focused on detecting FauxHate, where participants were tasked with de-veloping methodologies to identify this nu-anced form of harmful speech.

Depression has become a common health problem impacting millions of individuals globally. Workplace stress and an unhealthy lifestyle have increased in recent years, leading to an increase in the number of people experiencing depressive symptoms. The spread of the epidemic has further exacerbated the problem. Early detection and precise prediction of depression are critical for early intervention and support for individuals at risk. However, due to the social stigma associated with the illness, many people are afraid to consult healthcare specialists, making early detection practically impossible. As a result, alternative strategies for depression prediction are being investigated, one of which is analyzing users’ social media posting behaviour. The organizers of LT-EDI@RANLP carried out a shared Task to encourage research in this area. Our team participated in the shared task and secured 21st rank with a macro F1 score 0f 0.36. This article provides a summary of the model presented in the shared task.