Accepted for/Published in: JMIR Mental Health
Date Submitted: Sep 19, 2021
Date Accepted: Dec 26, 2021
Quantifying Language Changes Surrounding Mental Health on Twitter
ABSTRACT
Background:
Mental health challenges are thought to afflict around 10% of the global population each year, with many going untreated due to the stigma and limited access to services.
Objective:
Using messages from Twitter, we analyze the conversation around mental health and aim to quantify a hypothesized increase in discussions and awareness, as well as the corresponding reduction in stigma around mental illness.
Methods:
We explore trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of roughly 10% of all English tweets since 2012. We examine temporal dynamics of mental health language, as well as measure levels of positivity of messages. Finally, we use the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that is due to social amplification.
Results:
We find that the popularity of the phrase ‘mental health’ increased by nearly two orders of magnitude between 2012 and 2018. We observe that mentions of ‘mental health’ spike annually and reliably due to mental health awareness campaigns, as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional stories portraying suicide. We find that the level of positivity of messages containing ‘mental health’, while stable through the growth period, has declined recently. Finally, we observe that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that stigma associated with discussion of mental health on Twitter has diminished with time.
Conclusions:
These results provide useful texture regarding the growing conversation around mental health on Twitter and suggests that more awareness and acceptance has been brought to the topic compared to past years.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.