AI Model Detects Depression Signs in Reddit Posts with 96% Accuracy

AI can spot signs of depression in Reddit posts

A new study has found that artificial intelligence (AI) can now spot signs of depression in online writing, and can do so with high accuracy. The study, which was published in Corpus-Based Studies Across Humanities, found that models from machine learning (ML), a form of AI, could spot depression in posts on Reddit with 96% accuracy. The researchers say this study reveals how even short social media messages can reveal emotions. "Language is not just for conveying information," said Youngmeen Kim, a Ph.D. candidate in applied linguistics and the study's lead author. "It also shows how people feel. Even when someone does not say, 'I'm depressed,' their words can show pain." Kim and co-author Ute Römer-Barron, a Georgia State professor of applied linguistics, chose Reddit because users can post anonymously, and they often speak more openly there than on other sites. Kim said this stigma-free environment made Reddit a good place to study mental health discourse. Kim and Römer-Barron designed an ML model and analyzed 40,000 posts that came from two Reddit groups. One was r/depression, for mental health discourse. The other was r/relationship_advice, for everyday problems. Comparing the two helped them identify words and patterns associated with depression. Users in the depression forum used more first-person words (like "I" and "me"). They also used more phrases that show hopelessness, like "I don't know what to do." "Those short, direct phrases carry emotional weight," Kim said. "They show self-focus and isolation. Both are common in depression discourse." The researchers used LLM-based topic modeling, an artificial intelligence method that scans large amounts of text and clusters words that appear together to reveal themes. They saw talks about school, family and medicine. One pattern that stood out involved holidays. The model identified "Christmas," "birthday" and "Thanksgiving" as keywords that are often linked to isolation, stress or painful memories. "For many, days like Christmas are joyful," Kim said. "But according to our data, those days can be lonely and sad for some and exacerbate feelings of depression. This shows that times that should be enjoyable can trigger pain for some people." The study emphasized that while holidays don't cause depression, they may heighten feelings of isolation or stress, which can worsen depressive symptoms, a pattern also seen in earlier research linking social isolation and mental health risks. Römer-Barron said the project began as a class assignment in her Phraseology course, which explores how language patterns convey meaning. She encouraged Kim to expand his course paper into a study for publication. She explained that applied linguistics -- and corpus linguistics in particular -- gives researchers tools to study how people use language in everyday settings. By combining those tools with computational methods, she said, researchers can uncover patterns in large sets of natural language data that would be difficult to spot by hand. Kim added, "Future work will check if ML and AI approaches can identify language specific to anxiety or post-traumatic stress disorders as well. We will also see if results change across different languages and cultures." The goal of the study was not to diagnose people. But the researchers think their method could help create early warning systems to spot signs of depression. These systems would help social media moderators, wellness programs and health experts. "AI can't replace mental health experts," Kim said. "It should be viewed as a complement -- a tool to help identify people who may need support."

AI can spot signs of depression in online writing

A new study has found that artificial intelligence can now spot signs of depression in online writing, and can do so with high accuracy. The study in Corpus-Based Studies Across Humanities found that models from machine learning (ML), a form of AI, could spot depression in posts on Reddit with 96% accuracy. The researchers say this study reveals how even short social media messages can reveal emotions. "Language is not just for conveying information," says Youngmeen Kim, a PhD candidate in applied linguistics and the study's lead author. "It also shows how people feel. Even when someone does not say, 'I'm depressed,' their words can show pain." Kim and coauthor Ute Römer-Barron, a Georgia State professor of applied linguistics, chose Reddit because users can post anonymously, and they often speak more openly there than on other sites. Kim says this stigma-free environment made Reddit a good place to study mental health discourse. Kim and Römer-Barron designed an ML model and analyzed 40,000 posts that came from two Reddit groups. One was r/depression, for mental health discourse. The other was r/relationship_advice, for everyday problems. Comparing the two helped them identify words and patterns associated with depression. Users in the depression forum used more first-person words (like "I" and "me"). They also used more phrases that show hopelessness, like "I don't know what to do." "Those short, direct phrases carry emotional weight," Kim says. "They show self-focus and isolation. Both are common in depression discourse." The researchers used LLM-based topic modeling, an artificial intelligence method that scans large amounts of text and clusters words that appear together to reveal themes. They saw talks about school, family and medicine. One pattern that stood out involved holidays. The model identified "Christmas," "birthday," and "Thanksgiving" as keywords that are often linked to isolation, stress, or painful memories. "For many, days like Christmas are joyful," Kim says. "But according to our data, those days can be lonely and sad for some and exacerbate feelings of depression. This shows that times that should be enjoyable can trigger pain for some people." The study emphasizes that while holidays don't cause depression, they may heighten feelings of isolation or stress, which can worsen depressive symptoms, a pattern also seen in earlier research linking social isolation and mental health risks. Römer-Barron says the project began as a class assignment in her Phraseology course, which explores how language patterns convey meaning. She encouraged Kim to expand his course paper into a study for publication. She explains that applied linguistics -- and corpus linguistics in particular -- gives researchers tools to study how people use language in everyday settings. By combining those tools with computational methods, she says, researchers can uncover patterns in large sets of natural language data that would be difficult to spot by hand. Kim adds: "Future work will check if ML and AI approaches can identify language specific to anxiety or post-traumatic stress disorders as well. We will also see if results change across different languages and cultures." The goal of the study was not to diagnose people. But the researchers think their method could help create early warning systems to spot signs of depression. These systems would help social media moderators, wellness programs, and health experts. "AI can't replace mental health experts," Kim says. "It should be viewed as a complement -- a tool to help identify people who may need support."