Machine learning models to detect anxiety and depression through social media: A scoping review - Ahmed, Arfan; Aziz, Sarah; Toro, Carla T.; Alzubaidi, Mahmood; Irshaidat, Sara; Serhan, Hashem Abu; Abd-alrazaq, Alaa A.; Househ, Mowafa (Computer methods and programs in biomedicine update, 2022)
•We explored ML models used to detect anxiety and depression through social media.•A dramatic rise observed in reviewed studies during COVID-19 peak.•AI-technology based predictive models could offer the opportunity to identify symptoms at an earlier stage.•ML models on social media texts utilized as predictive models for the detection of depression or anxiety.•AI has potential in complimenting traditional screening. 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.