Lunch at 12:30pm, talk at 1pm, in 148 Fitzpatrick

Title: Language Models for Online Depression Detection: A Review and Benchmark Analysis on Remote Interviews

Abstract: The use of machine learning (ML) to detect depression in online settings has emerged as an important health and wellness use case. In particular, the use of deep learning methods for depression detection from textual content posted on social media has garnered considerable attention. Conversely, there has been relatively limited evaluation of depression detection in clinical environments involving text generated from remote interviews. In this research, we review state-of-the-art feature-based ML, deep learning, and large language models for depression detection. We use a multi-dimensional analysis framework to benchmark various language models on a novel testbed comprising speech-to-text transcriptions of remote interviews. Our framework considers the impact of different transcription types and interview segments on depression detection performance. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of future detection methods.

Bio: Ryan Cook received a BBA in Analytics with a minor in Philosophy from Notre Dame, and an MS in Computer Science from the University of Pennsylvania. He worked as a research scientist in Notre Dame’s Human-centered Analytics Lab and Center for Computer Assisted Synthesis, supporting projects related to NLP and network analysis. Ryan was also previously an analyst at EY in Chicago.