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Software used to screen social media photos for depression signs

4:30pm Tuesday 8th August 2017 content supplied byNHS Choices

They then compared these features between the two groups and ran various computer programmes to see if they could predict who had depression based on 100 of their Instagram posts.

They compared their predictions with those made by GPs using data from a previous independent meta-analysis, which found GPs are able to correctly diagnose 42% of people with depression without using any validated questionnaires or measurements.

The Center for Epidemiologic Studies Depression Scale (CES-D) questionnaire was used as a screening tool for depression.

This uses a scale of 0-60 - it's generally considered that a score of 16 or more indicates a likely diagnosis of depression. People with a score of 22 or more were excluded from this study.

To see if humans are able to identify factors that computers cannot, the researchers also asked a sample of online users to each rate 20 randomly selected photographs on a scale of 0-5 on the following measurements:

  • happiness
  • sadness
  • interest
  • likeability

In all, 13,184 images were rated, with each image being rated by at least three people.

What were the basic results?

The computer programme identified 70% of the people with depression. It incorrectly identified 24% of people as having depression who did not.

The results were much less accurate for predicting depression before it had been diagnosed.

According to the computer-generated results, people in the depressed group were more likely to post:

  • photos that were bluer, darker and less vibrant
  • photos that generated more comments but fewer likes
  • more photos
  • photos with faces
  • photos without using filters

If they did use filters, they were more likely to use "inkwell", which converts photos to black and white, whereas the healthy controls were more likely to use "valencia", which brightens images.

The human responses to the photos found people who were in the depression group were more likely to post sadder and less happy images. Whether the images were likeable or interesting didn't differ between the groups.

How did the researchers interpret the results?

The researchers concluded: "These findings support the notion that major changes in individual psychology are transmitted in social media use, and can be identified via computational methods."

They say this early analysis could inform "mental health screening in an increasingly digitalised society". They acknowledge that further work on the ethical and data privacy aspects would be required.

Conclusion

This study suggests that a computer algorithm could be used to help screen for depression more accurately than GPs - using Instagram images.

But there are several limitations that need to be considered when analysing the results:

  • As only people with a CES-D score of between 16 and 22 (on a scale of 0-60) were included, this is likely to have ruled out those with moderate to severe depression.
  • There were a small number of participants.
  • Selection bias will have skewed the results - it only includes people who like to use Instagram and are willing to allow researchers access to all of their posts.
  • Many potential participants refused to take further part in the research once they realised they'd have to share their posts.
  • It relied on self-reporting of depression rather than formal diagnoses.
  • The data is all from US participants, so may not be generalisable to the UK.
  • The 100 posts from people with depression were analysed if they were within a year (before and after) of the diagnosis. As we don't know how long people may have had symptoms for before diagnosis and whether their symptoms had improved, it's difficult to make any accurate conclusions.
  • We don't know their lifelong preferences for colours or genre when posting images.
  • And, most importantly, the figure quoted that GP diagnostic accuracy was only at 42% was based on meta-analysis of studies where GPs were asked to diagnose depression without using questionnaires, scales or other measurement tools. This doesn't give a very realistic representation of depression diagnosis in normal clinical practice. As such, it can't be assumed that this model would be an improvement over standard methods for depression screening or diagnosis.  

Though the results of this study are interesting, it's unclear what benefits or risks may be attached to any future use of screening tools for depression using Instagram or other social media.

If you're concerned that you're depressed, it's best to contact your GP - there are a variety of effective treatments available.

Read more about seeking advice about low mood and depression.

Summary

"The images you put up on Instagram could be used to diagnose if you're depressed," the Mail Online reports. Researchers attempted to see if computer-driven image recognition could diagnose depression based on.

Links to Headlines

Can you tell which of these pictures reveals you are on the verge of depression? How the images that you post on Instagram reveal more than you realise about your state of mind. Mail Online, August 8 2017

A blue mood can colour Instagram posts, study finds. ITV News, August 8 2017

Posting lots of photos on social media? It could be a sign of depression. The Daily Telegraph, August 8 2017

Links to Science

Reece AG, Danforth CM. Instagram photos reveal predictive markers of depression. EPJ Data Science. Published online August 8 2017

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