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Machine learning models to detect anxiety and depression through social media: A scoping review. | LitMetric

AI Article Synopsis

  • Mental health disorders like anxiety and depression have become more prevalent since the COVID-19 pandemic, with social media serving as a platform where symptoms are often noted.
  • A review of 54 studies utilized machine learning models to detect these disorders by analyzing users' online language and activities across various social media platforms.
  • These models, predominantly developed during the pandemic, have the potential to complement traditional mental health screenings, offering insights into public mental health during times when access to healthcare may be limited.

Article Abstract

Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019-2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9461333PMC
http://dx.doi.org/10.1016/j.cmpbup.2022.100066DOI Listing

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