Social Media Data Tells Mental Status

Mood disorders such as anxiety and depression can be invisible but take a heavy toll on one’s life daily. Growing up in a culture that mental health is never discussed, I witness one of my parents struggled with these conditions over the years but barely received support from families and friends.  Symptoms that are typical to these conditions are often attributed to as having a ‘bad’ personality, and disclosing life difficulties is considered as weak and shameful. “You don’t need others to know you are having a bad day. People will just think that you are incapable of handling your own issues.” This is a common coping strategy in a culture that suppresses negative emotions.


20 years later, we have lots of online mental health support discussion forums. People who are living with these conditions in a mental illness stigmatized culture can seek support anonymously from strangers who care about their conditions. A big question is, how do I know if I have mood disorders without going to see a doctor? The answer is no, you can’t. Mental illness diagnosis is difficult, part of the reasons is because the current diagnostic criteria that have been used for more than half a century is simply grouping the symptoms together but not considering the underlying biological mechanism. Diagnosing a physical illness is to identify the problematic organ, symptoms may be different but they all stem from a malfunction of a particular organ or multiple organs. However, it’s usually very difficult to locate the cause of mental illnesses. It could be originated from hormone issues, brain injuries, sudden adverse life events, childhood trauma, genetic components and so on. Doctors have been trained for years to learn the symptoms case by case over hundreds and thousands of hours of conversation with patients. Deciding whether the symptoms are more similar to one type of disorder or another is difficult even for doctors, don’t mention the current AI that base on some social media records.
However, AI cannot make a diagnosis doesn’t mean that it can not support a human decision. In the case that you don’t want to go for a diagnosis before you know something is off, AI can probably help with the early detection. We are now developing technologies that help people to understand if they have shown signs of mood disorders based on social media records. These technologies might ring the alarm bell in your mind that this is the time you should sign up for an anonymous online support group even if you really don’t want to see a doctor because you are afraid your family and friends will find out. In our work satisfaction Building a profile of subjective well-being for social media users and Inspecting Vulnerability to Depression From Social Media Affect we found that social media data contain signals that can be used to infer one’s subjective well being and mental health status. During my PhD, I especially focus on social media signals that may be directly associated with symptoms of affective disorders. The emotions and feelings of a person are called ‘affect’ in psychology. Affective pattern is an important signal for affective (mood) disorders, such as anxiety and depression. In the paper The Effect of User Psychology on the Content of Social Media Posts: Originality and Transitions Matter (accepted), we found the transition states of valence (not just its magnitude and frequency) reflect one’s personality and mental health status. We found that extroverted participants are more likely to transition from a positive mood to a positive mood. In a paper we submitted two months ago, we constructed a mood profile for social media users based on their posts, we the mood profile is highly relevant to the level of depressive symptoms.

Author: Lucia

I’m a Ph.D. student at the University of Edinburgh, School of Informatics. My research project involves using digital data to track the affective, cognitive, and behavioral changes of users, identifying the associations between digital signals and symptoms of mental disorders. I am interested in early symptom detection as a human-centered approach to assist interventions and early prevention of mental disorders or harmful behaviors. Along the way, I deeply care about ethical research practices, model bias and fairness. My work involves understanding model biases and examining the ‘noise’ in social media signals. I am writing up my Ph.D. thesis at the moment and looking for a post-doctoral research position. I am also passionate to communicate my research and machine learning methods. Check out my YouTube channel: ML_made_simple

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