Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses

Abstract

Evidence has demonstrated the presence of similarities in language use across people with various mental health conditions. In this work we investigate these relationships both as described in literature and as a data analysis problem. We also introduce a novel transfer learning based approach that learns from linguistic feature spaces of previous conditions and predicts unknown ones. Our model achieves strong performance, with F 1 scores of 0.75, 0.80, and 0.76 at detecting depression, stress, and suicidal ideation in a first-of-its-kind transfer task and offering promising evidence that language models can harness learned patterns from known mental health conditions to aid in their prediction of others that may lie latent.

Unmasking AI-Generated Fake News Across Multiple Domains

“…This makes distinguishing between genuine and AI-generated information exceedingly difficult. Historically, numerous studies have probed the dangers of misinformation produced by neural networks, suggesting that it is crafted by specific models [36][37][38][39][40][41][42][43][44], or focusing on the identification of such neural misinformation [45][46][47][48][49][50]. Recently, there have been some preliminary studies focused on misinformation generated by LLMs [51][52][53][54][55].…”