Social media posts could be deemed signs of mental illness
by Harini Barath, Dartmouth College
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| This tweet from Blake "Flip" Filippi, Charlestown's state rep, is a good example. The post is about a bogus right-wing conspiracy theory making the rounds that FEMA's response to a nuclear disaster is to impose social distancing. That is a distortion of the FEMA guidance in all emergencies where people must shelter together to be mindful of the spread of COVID. One of the people pushing this fake news the hardest is Donald Trump Jr. |
What sets the new model apart is a focus on the emotions rather than the specific content of the social media texts being analyzed.
In a paper presented at the 20th International Conference on Web Intelligence and Intelligent Agent Technology, the researchers show that this approach performs better over time, irrespective of the topics discussed in the posts.
There are many reasons why people don't seek help for mental health disorders—stigma, high costs, and lack of access to services are some common barriers. There is also a tendency to minimize signs of mental disorders or conflate them with stress, says Xiaobo Guo, Guarini '24, a co-author of the paper. It's possible that they will seek help with some prompting, he says, and that's where digital screening tools can make a difference.
"Social media offers an easy way to tap into people's behaviors," says Guo. The data is voluntary and public, published for others to read, he says.
Reddit, which offers a massive network of user
forums, was their platform of choice because it has nearly half a billion
active users who discuss a wide range of topics. The posts and comments are
publicly available, and the researchers could collect data dating back to 2011.
In their study, the researchers focused on
what they call emotional
disorders—major depressive, anxiety, and bipolar disorders—which are
characterized by distinct emotional patterns. They looked at data from users
who had self-reported as having one of these disorders and from users without
any known mental disorders.
They trained their model to label the emotions
expressed in users' posts and map the emotional transitions between different
posts, so a post could be labeled "joy," "anger,"
"sadness," "fear," "no emotion," or a combination
of these. The map is a matrix that would show how likely it was that a user
went from any one state to another, such as from anger to a neutral state of no
emotion.
Different emotional disorders have their own
signature patterns of emotional transitions. By creating an emotional
"fingerprint" for a user and comparing it to established signatures
of emotional disorders, the model can detect them. To validate their results,
they tested it on posts that were not used during training and show that the
model accurately predicts which users may or may not have one of these
disorders.
This approach sidesteps an important problem
called "information leakage" that typical screening tools run into,
says Soroush Vosoughi, assistant professor of computer science and another
co-author. Other models are built around scrutinizing and relying on the
content of the text, he says, and while the models show high performance, they
can also be misleading.
For instance, if a model learns to correlate
"COVID" with "sadness" or "anxiety," Vosoughi
explains, it will naturally assume that a scientist studying and posting (quite
dispassionately) about COVID-19 is suffering from depression or anxiety. On the
other hand, the new model only zeroes in on the emotion and learns nothing
about the particular topic or event described in the posts.
While the researchers don't look at
intervention strategies, they hope this work can point the way to prevention.
In their paper, they make a strong case for more thoughtful scrutiny of models
based on social media data. "It's very important to have models that
perform well," says Vosoughi, "but also really understand their
working, biases, and limitations."