Presentation Title
Word Embeddings to quantify Depressive Language in Twitter
Abstract
How do people discuss mental health on social media? Can we train an algorithm to recognize differences between discussions of depression and other topics? Can an algorithm predict that someone is depressed from their tweets alone? In this project, we collect tweets referencing `depression’ over a seven year period, and train word embeddings to characterize linguistic structures within the corpus. We find that neural word embeddings capture the contextual differences between “depressed” and “healthy” language. The best performing model for the prediction task is Long Short-Term Memory (LSTM) with 70% test accuracy. Finally, we train a similar model on a much smaller collection of tweets authored by individuals formally diagnosed with depression. The results suggest social media could serve as a valuable screening tool for mental health.
Primary Faculty Mentor Name
Christopher M. Danforth
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Second Student College
College of Engineering and Mathematical Sciences
Program/Major
Computer Science
Second Program/Major
Complex Systems
Primary Research Category
Health Sciences
Secondary Research Category
Social Sciences
Word Embeddings to quantify Depressive Language in Twitter
How do people discuss mental health on social media? Can we train an algorithm to recognize differences between discussions of depression and other topics? Can an algorithm predict that someone is depressed from their tweets alone? In this project, we collect tweets referencing `depression’ over a seven year period, and train word embeddings to characterize linguistic structures within the corpus. We find that neural word embeddings capture the contextual differences between “depressed” and “healthy” language. The best performing model for the prediction task is Long Short-Term Memory (LSTM) with 70% test accuracy. Finally, we train a similar model on a much smaller collection of tweets authored by individuals formally diagnosed with depression. The results suggest social media could serve as a valuable screening tool for mental health.