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Forecasting the onset and course of mental illness with Twitter data

Overview of attention for article published in Scientific Reports, October 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
8 news outlets
blogs
2 blogs
twitter
451 tweeters
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
49 Dimensions

Readers on

mendeley
198 Mendeley
Title
Forecasting the onset and course of mental illness with Twitter data
Published in
Scientific Reports, October 2017
DOI 10.1038/s41598-017-12961-9
Pubmed ID
Authors

Andrew G. Reece, Andrew J. Reagan, Katharina L. M. Lix, Peter Sheridan Dodds, Christopher M. Danforth, Ellen J. Langer

Abstract

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners' average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness.

Twitter Demographics

The data shown below were collected from the profiles of 451 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 198 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
India 1 <1%
Sri Lanka 1 <1%
Luxembourg 1 <1%
Unknown 194 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 20%
Researcher 32 16%
Student > Bachelor 29 15%
Student > Master 29 15%
Student > Doctoral Student 11 6%
Other 34 17%
Unknown 24 12%
Readers by discipline Count As %
Computer Science 62 31%
Psychology 32 16%
Social Sciences 14 7%
Medicine and Dentistry 14 7%
Engineering 10 5%
Other 33 17%
Unknown 33 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 387. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 January 2020.
All research outputs
#33,683
of 15,166,268 outputs
Outputs from Scientific Reports
#462
of 78,134 outputs
Outputs of similar age
#1,346
of 263,676 outputs
Outputs of similar age from Scientific Reports
#20
of 3,537 outputs
Altmetric has tracked 15,166,268 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 78,134 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,676 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 3,537 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.