The information we post online could reveal insights into our mental health.
In fact, according to US experts, it may spot key symptoms of depression and low-mood – months before a doctor’s formal diagnosis.
Researchers believe an algorithm could potentially scan a person’s social media posts and alert them to linguistic red flags which are symptomatic of the condition.
Indicators of the condition included mentions of hostility and loneliness, words like ‘tears’ and ‘feelings’, plus use of more first-person pronouns like ‘I’ and ‘me’.
Insight: Indicators of the condition included mentions of hostility and loneliness, words like ‘tears’ and ‘feelings’, plus use of more first-person pronouns like ‘I’ and ‘me’
Researchers from the University of Pennsylvania and Stony Brook University published their work in the Proceedings of the National Academy of Sciences.
‘What people write in social media and online captures an aspect of life that’s very hard in medicine and research to access otherwise,’ says H. Andrew Schwartz, senior paper author and a principal investigator of the World Well-Being Project.
‘It’s a dimension that’s relatively untapped compared to biophysical markers of disease. Considering conditions such as depression, anxiety, and PTSD, for example, you find more signals in the way people express themselves digitally.’
For six years, the WWBP – which is based between Penn’s Positive Psychology Center and Stony Brook’s Human Language Analysis Lab – has been studying how certain words reflect inner feelings.
‘Social media data contain markers akin to the genome,’ Johannes Eichstaedt, WWBP founding research scientist, said.
‘With surprisingly similar methods to those used in genomics, we can comb social media data to find these markers. Depression appears to be something quite detectable in this way; it really changes people’s use of social media in a way that something like skin disease or diabetes doesn’t.’
Comprehensive: To build the algorithm, Eichstaedt and colleagues looked back at 524,292 Facebook updates from the years leading to diagnosis for each individual with depression
Nearly 1,200 people consented to provide digital archives of their Facebook use and medical records. Of these, just 114 people had a diagnosis of depression in their medical records.
The researchers then matched every person with a diagnosis of depression with five who did not have such a diagnosis, to act as a control, for a total sample of 683 people (excluding one for insufficient words within status updates).
To build the algorithm, Eichstaedt and colleagues looked back at 524,292 Facebook updates from the years leading to diagnosis for each individual with depression.
They determined the most-frequently used words and phrases, then modeled 200 topics to suss-out what they called ‘depression-associated language markers.’
Finally, they compared in what manner and how frequently depressed versus control participants used such phrasing.
‘There’s a perception that using social media is not good for one’s mental health,’ Schwartz says, ‘but it may turn out to be an important tool for diagnosing, monitoring, and treating it.’
They learned that these markers comprised emotional, cognitive, and interpersonal processes such as hostility and loneliness, sadness and rumination, and that they could predict future depression as early as three months before first documentation of the illness in a medical record.
‘There’s a perception that using social media is not good for one’s mental health,’ Schwartz says, ‘but it may turn out to be an important tool for diagnosing, monitoring, and eventually treating it. Here, we’ve shown that it can be used with clinical records, a step toward improving mental health with social media.’
Eichstaedt sees long-term potential in using these data as a form of unobtrusive screening.
‘The hope is that one day, these screening systems can be integrated into systems of care. This tool raises yellow flags; eventually the hope is that you could directly funnel people it identifies into scalable treatment modalities.’
Facebook and Twitter could lead to a mental health timebomb
The more time young adults use social media, the more likely they are to be depressed, according to research from the University of Pittsburgh School of Medicine.
They say social media sites could be fueling ‘Internet addiction,’ a proposed psychiatric condition closely associated with depression.
The findings could guide clinical and public health interventions to tackle depression, forecast to become the leading cause of disability in high-income countries by 2030.
‘Because social media has become such an integrated component of human interaction, it is important for clinicians interacting with young adults to recognize the balance to be struck in encouraging potential positive use, while redirecting from problematic use,’ said Brian Primack, director of Pitt’s Center for Research on Media, Technology and Health.
‘It may be that people who already are depressed are turning to social media to fill a void,’ said said lead author Lui yi Lin of the University of Pittsburgh.
Ms. Lin said exposure to social media also may cause depression, which could then in turn fuel more use of social media.
She warned exposure to highly idealized representations of peers on social media elicits feelings of envy and the distorted belief that others lead happier, more successful lives.
The research also found engaging in activities of little meaning on social media may give a feeling of ‘time wasted’ that negatively influences mood.
The research, funded by the National Institutes of Health, was published online in the April 1 2016 issue of the journal Depression and Anxiety.