Date of Award
2019
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Christopher Danforth
Second Advisor
Kelly Rohan
Abstract
How do people discuss mental health on social media? Can we train a computer program 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” and “depressed” 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. We also looked at how context around words may have changed over time to get deeper understanding of contextual shifts in the word usage. Finally, we trained a deep learning network on a much smaller collection of tweets authored by individuals formally diagnosed with depression. The best performing model for the prediction task is Convolutional LSTM (CNN-LSTM) model with a F-score of 69% on test data. The results suggest social media could serve as a valuable screening tool for mental health.
Language
en
Number of Pages
61 p.
Recommended Citation
Gopchandani, Sandhya, "Using Word Embeddings to Explore the Language of Depression on Twitter" (2019). Graduate College Dissertations and Theses. 1072.
https://scholarworks.uvm.edu/graddis/1072