Could X Help Detect Depression?
Using social media as a tool for good.
Woman holding an arm in front of her eyes.
Whenever you hear about social media in the news, it's almost always something bad. There's a reason for this of course—studies show social media can interfere with teens’ sleep, contribute to anxiety, and reinforce filter bubbles—but all the doom and gloom can get a bit depressing.
For better or worse, though, social media isn’t going anywhere. So let’s change the perspective: As we improve the platforms, why not put their reach to good use?
Researchers at Microsoft had a similar line of thinking in 2013 when they published a study called "Predicting Depression via Social Media." In it, the researchers explained how they created a model that used X to predict depression.
Let’s unpack the study’s findings and what it could mean for social media users!
How Could You Tell if Someone is Depressed on X?
The researchers created a list of X users with severe depression and users who showed little or no signs of the conditions based on a common screening questionnaire. They then compared the tweets that the severely depressed respondents to tweets the other respondents posted. In total, they analyzed a whopping 2,157,992 tweets.
Signs of depression on X:
Less social activity
More negative emotion
Increased focus on self
Greater medical concerns
Increased interest in religion
They came to this conclusion by comparing several factors, including the use of depression-related terms and how participants engaged with X. As an example, a tweet from a study participant with severe depression read:
"Having a job again makes me happy. Less time to be depressed and eat all day while watching sad movies."
The research team used this data to create a model that could predict the likelihood that participants would develop depression with 70% accuracy!
Woman standing next to a large sad face, denoting depression
How the Depression-Detecting Model Works
Analyzing the word choice of tweets.
Considering the number of followers and followees.
The number of outgoing and incoming messages.
The times of day when users post.
Would a Depression-Detecting App Work?
While these findings sound promising, would it work in our current climate?
After all, the tendency to only post your best moments online—your highlight reel—has intensified since this research was published. If people are not being honest about how they feel online, would a model like the one created by the Microsoft researchers even work?
Well, the model considers factors less affected by the highlight reel phenomenon. It could potentially work around the mirage most people display online. Before it could stand a chance at being trialled in real life, though, the model would have to be tweaked to be even more accurate in its predictions, accounting for when people are masking how they’re doing.
A scale of faces depicting emotions from upset to happy.
Privacy is another concern. A model that uses X to flag depression around the world would be incredible, but would people want something trawling through their tweets?
The research team saw their findings utilized in a privacy-preserving app that provides users with information about professional help and the value of support when it predicts that an extreme change in their mood is likely. This could involve having users opt-in and ensuring no personal information is recorded.
A somewhat similar concept is already in place on TikTok, where users are shown a video that encourages them to take a break if they’ve been scrolling on the app for too long.
Could Social Media Help People with Depression?
In short, yes!
We have some reservations, namely accuracy and privacy, about a feature on social media that could identify users who may be on the verge of developing a mental illness. Yet, it sounds like an avenue worth exploring further.
According to the Canadian Mental Health Association, 50% of people will have or have had a mental illness by the age of 40. And according to some studies, there’s a link between social media and anxiety and depression, which needs to be addressed.
While we work to make social networking platforms better, we could use them to meet people where they’re at and equip them with knowledge that could truly effect change. Now that’s something worth tweeting about.
Study Objective & Methods
Predicting Depression via Social Media
Munmun De Choudhury, PhD; Michael Gamon, PhD; Scott Counts, PhD; Eric Horvitz; PhD
Published in Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, 2013.
In this study, De Choudhury, Gamon, Counts, and Horvitz aimed to examine the potential of using social media as a tool to detect and predict depression. To do this, De Choudhury’s team used crowdsourcing to compile a list of 476 Twitter users who (1) agreed to give the researchers access to their Twitter feeds, (2) indicated that they began experiencing depression symptoms in the last year, (3) reported that they experienced at least two depressive episodes in the last year, and (4) filled out two depression screening tests that yielded similar results. One of the two tests, the CES-D questionnaire, typically calculates three groups of depression severity: low, mild to moderate, and high. The researchers decided to use the high category as the threshold for having depression in their study in order to minimize false negatives and false positives; 171 survey respondents fell into this category. De Choudhury’s team then compared the tweets they posted in the year before their symptoms started to tweets the 305 remaining survey respondents posted in the year before taking the survey. The measures they used to compare the tweets were: user engagement, emotion, egocentric social graph, linguistic style, and depressive language use. The researchers used the results to build a Support Vector Machine classifier that predicted the likelihood of depression ahead of the onset with an accuracy of 70% and a precision of 0.74.
Social Media and Well-Being Training
This research (and all our social media and well-being articles) have laid the foundation for our 3-course program designed for anyone wanting to approach social media and communications in a way that protects well-being and puts people first. Learn more here.
