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Analysis

Twitter and Mental Health Diagnoses

Something Mindful Data has been thinking about for quite some time: what people are saying on Twitter in association to some of the most major mental illnesses. Unfortunately, the organization doesn’t have access (nor the money) to pay for a Twitter Partnership, so a query of the last 2,000 tweets were pulled. Might create a dashboard though.

What’s the point of this analysis? Namely, text analysis of publicly-facing content by individuals can find insights and help provide the field ways to consolidate language in order to get advocacy and de-stigmatization better across to the larger population. Based on previous analyses of Mental Health Advocates, support to fill that gap is lacking. To visualize language of these tweets, an analysis with WordClouds and basic text analysis was done.

DepressionBipolarSchizophrenia
Avg. Polarity
(-1 to 1)
0.052
0.011
0.050

Avg. Subjectivity
(0 to 1)
0.3600.3610.317
Avg. Verbosity (0 to 140)107.994.2120.3

More info on the background of the Sentiment analysis via TextBlob can be found on this exceptional community post. So, what’s learned?

  1. On average, the sentiment is surprisingly positive.
  2. On average, the sentiment is surprisingly more objective than subjective.
  3. There’s a difference in verbosity between tweets analyzed related to various MI’s and SMI’s.

What might better help Mental Health advocates on Twitter with this analysis? The most important take-away is the visualization and analysis from the Word Clouds and Verbosity scores. Notice that in Schizophrenia, there is little popularity in most words except the diagnosis term itself compared to Depression and Bipolar. The basic assumption is that Twitter users don’t have a shared language on how that diagnosis is communicated. The Schizophrenia/Psychosis community would lend itself to learn to have a shared language that’s not mixed and verbose.

Scripts found here.