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This Twitter algorithm predicts mental illness better than trained professionals

supervised learning algorithm can predict clinical depression much earlier and more accurately than trained health professionals.

Aaron Gillies, a.k.a @TechnicallyRon, uses his sense of humor to open up frank discussion about depression.

One of the more surprising, and upsetting, uses of social media has been suicides performed on Facebook Live. Though reasons for suicide are complex, the mere threat is often a cry for help, acceptance, or recognition. During the two years I worked as a patient monitor in an emergency room, I discovered most people that attempt to take their own lives desire a pair of ears to listen to their problems more than anything else.

It's hard to gauge a person's reality based on social media habits, however. Those who spout vitriolic rhetoric are often quite approachable and reserved in person. We can't read inflections and temperament from words on a screen, or take into consideration that the person might just be having a bad day.

That said, social media can be a powerful indicator of those at risk for suffering from mental health disorders, a new study published in Scientific Reports suggests. A team led by Andrew Reece, in the Department of Psychology at Harvard, collected Twitter data from 204 individuals. Of those, 105 suffered from depression, with a control of 99 healthy subjects. The team then used a supervised learning algorithm to see if changes in language predicted clinical depression.

The answer is yes. Depressed patients used more words like death, no, and never, while posting fewer positive words—like happy, beach, and photo—in the lead-up to their diagnosis.

Figure 4. Depression word-shift graph revealing contributions to difference in Twitter happiness observed between depressed (5.98) and healthy (6.11) participants. In column 3, (−) indicates a relatively negative word, and (+) indicates a relatively positive word, both with respect to the average happiness of all healthy tweets. An up (down) arrow indicates that word was used more (less) by the depressed class. Words on the left (right) contribute to a decrease (increase) in happiness in the depressed class. [Credit: Andrew G. Reece et. al.]

A second group pf 174 Twitter users was also studied. Of these, 63 suffered from PTSD. Again, changes in language revealed that they were likely to be diagnosed.

These results are not perfect. In both situations there was a preselected pool of Twitter users with a close ratio of healthy to unhealthy, which does not reflect society as a whole. Add to this fact that many depressed people or those suffering from PTSD do not use social media. It would be hard to acquire firm numbers based on these shortcomings.

That said, Reece and his team are borrowing this predictive model from similar early warning systems in place for hard-to-detect cancers, disease outbreaks, and regional dietary health issues. Diseases like addiction and suicidal ideation have already been studied through social media. While this trend of using public facing data to detect potential cognitive disorders is new, cries for help might be detected, and treated, much sooner.

Reece and team believe they have found if not a silver bullet for predicting depression and PTSD, at least a shinier one than has so far been developed:

Our findings strongly support the claim that computational methods can effectively screen Twitter data for indicators of depression and PTSD. Our method identified these mental health conditions earlier and more accurately than the performance of trained health professionals, and was more precise than previous computational approaches.

With the current rise in depression and anxiety, especially among teens, a particularly vulnerable group that has now fully grown up on social media, such predictive tools could prove to be a valuable source of therapy and recovery moving forward.

"We hope that our research will eventually help improve mental health care, for example in preventive screening," Stanford researcher Katharina Lix told Digital Trends. “We could imagine clinicians using this technology as a supporting tool during a patient's initial assessment, provided that the patient has agreed to have their social media data used in this way. However, before we get to that point, the technology needs to be validated using a larger sample of people that's representative of the general population. We want to emphasize that any real-world application of this technology must carefully take into account ethical and privacy concerns."

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Derek is the author of Whole Motion: Training Your Brain and Body For Optimal Health. Based in Los Angeles, he is working on a new book about spiritual consumerism. Stay in touch on Facebook and Twitter.

Is the universe a graveyard? This theory suggests humanity may be alone.

Ever since we've had the technology, we've looked to the stars in search of alien life. It's assumed that we're looking because we want to find other life in the universe, but what if we're looking to make sure there isn't any?

According to the Great Filter theory, Earth might be one of the only planets with intelligent life. And that's a good thing (NASA, ESA, and the Hubble Heritage Team [STScI/AURA]).
Surprising Science

Here's an equation, and a rather distressing one at that: N = R* × fP × ne × f1 × fi × fc × L. It's the Drake equation, and it describes the number of alien civilizations in our galaxy with whom we might be able to communicate. Its terms correspond to values such as the fraction of stars with planets, the fraction of planets on which life could emerge, the fraction of planets that can support intelligent life, and so on. Using conservative estimates, the minimum result of this equation is 20. There ought to be 20 intelligent alien civilizations in the Milky Way that we can contact and who can contact us. But there aren't any.

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The key to better quality education? Make students feel valued.

Building a personal connection with students can counteract some negative side effects of remote learning.

Future of Learning
  • Not being able to engage with students in-person due to the pandemic has presented several new challenges for educators, both technical and social. Digital tools have changed the way we all think about learning, but George Couros argues that more needs to be done to make up for what has been lost during "emergency remote teaching."
  • One interesting way he has seen to bridge that gap and strengthen teacher-student and student-student relationships is through an event called Identity Day. Giving students the opportunity to share something they are passionate about makes them feel more connected and gets them involved in their education.
  • "My hope is that we take these skills and these abilities we're developing through this process and we actually become so much better for our kids when we get back to our face-to-face setting," Couros says. He adds that while no one can predict the future, we can all do our part to adapt to it.
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Study details the negative environmental impact of online shopping

Frequent shopping for single items adds to our carbon footprint.

A truck pulls out of a large Walmart regional distribution center on June 6, 2019 in Washington, Utah.

Photo by George Frey/Getty Images
Politics & Current Affairs
  • A new study shows e-commerce sites like Amazon leave larger greenhouse gas footprints than retail stores.
  • Ordering online from retail stores has an even smaller footprint than going to the store yourself.
  • Greening efforts by major e-commerce sites won't curb wasteful consumer habits. Consolidating online orders can make a difference.
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Personal Growth

Childhood sleeping problems may signal mental disorders later in life

Chronic irregular sleep in children was associated with psychotic experiences in adolescence, according to a recent study out of the University of Birmingham's School of Psychology.

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