SC Connecticut News

A person’s choice of words can be predictive of worsening symptoms of major depressive disorder, a new Yale study finds. Using both human evaluators and the large language model ChatGPT, researchers demonstrated that written responses to open-ended questions could be used to predict who would experience worse symptoms of depression weeks later.

The findings, reported Sept. 16 in the Proceedings of the National Academy of Sciences, suggest automated procedures that can assess language use might complement and enhance psychological evaluations.

A growing body of research has uncovered a link between depression and the language a person uses. People with depression use more negative emotional words on social media and in text messages, for instance. And word choice is associated with how well individuals respond to treatment.

"For this study, Yale researchers wanted to explore whether language might also yield insight into someone’s future symptoms," said Robb Rutledge, an assistant professor of psychology in Yale’s Faculty of Arts and Sciences and senior author of the study. To better understand this, they asked 467 participants to complete nine open-ended, neutral short-answer questions and the Patient Health Questionnaire (PHQ-9), which assesses depression severity. Three weeks later, all participants completed the PHQ-9 questionnaire again.

Using a tool called Linguistic Inquiry and Word Count (LIWC) — which can calculate how many words fall into a particular category — the researchers identified how many words in the participants’ written responses to the short-answer questions had a positive or negative emotional tone. While LIWC scores were associated with depression severity at the time participants answered the questions, they did not predict depression severity three weeks later, researchers found.

Sentiment scores given by human raters, on the other hand, did predict future depression symptoms.

"This told us that human raters were picking up on something that just counting emotion words could not," said Rutledge.

LIWC treats each word individually, which may be why it falls short in this particular application, said the researchers.

"We wanted to look at word order and the multidimensional aspect of language central to shaping emotional tone," said lead author Jihyun Hur, a Ph.D. student in Rutledge’s lab and the lab of coauthor Jutta Joormann, the Richard Ely Foundation Professor of Psychology. "That’s when we got interested in ChatGPT."

ChatGPT is an artificial intelligence tool that aims to mimic human conversational speech. Therefore, word order and meaning within and between phrases are taken into account in a way that standard tools for analyzing language do not.

When the researchers instructed ChatGPT versions 3.5 and 4.0 to rate the positive and negative tone of participants’ responses, these scores predicted future changes in depression severity much like human raters’ scores.

Researchers say this finding lays a foundation for additional research. Rutledge's team is interested in how this approach might be applied to other psychiatric disorders over longer periods. This line of work is part of ongoing research into emotion and decision-making relationships through their free smartphone app Happiness Quest.

Rutledge envisions this type of language assessment as a useful addition to clinicians' tools: "You want a combination of tools that work across lots of people... If some are automated like this, it frees up clinicians to spend more time trying to help patients."

"Artificial intelligence tools like ChatGPT open up a new way to use available clinical language data for better mental health understanding," added Hur.