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A group of Johns Hopkins computer scientists think that data mining Twitter can lead to insights in common mental illnesses. It would work in a way similar to that used to map disease outbreaks such as flu, but this time focus on depression and other mental illnesses.
The technique involves mining Twitter streams for mention of mental health diagnosis in individual tweets and then adding that account to a database to be followed. With several thousand of these, the data mining can extrapolate tweets and analyze the streams overall to discover language cues that are linked to certain disorders.
A test run of the idea gave them a quick, inexpensive data collection on post-traumatic stress disorder, depression, bipolar disorder, and seasonal affective disorder. Their research was presented at three scientific conferences over the year, showing that the analysis could provide a much less difficult way to collect valuable data for analysis.
The data collection, of course, scrubbed all names associated with the tweets in order to preserve privacy and did not access private messages between users, only public posts.
Using computer technology to sift through tweets, they said, can help address the slow pace and high costs associated with collecting mental health data through surveys and other traditional methods. The researchers point out that they aren't aiming to replace traditional surveys, but instead to augment them or provide a new avenue for analysis.
The initial tests of the method showed that "mapping" instances of PTSD, for example, showed that it was more prevalent in and around military bases where deployments to conflict areas were common, proving the assumption usually made about PTSD among psychiatrists.
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