Detecting paranoia among social media users
Artificial intelligence and text mining techniques can be used to detect paranoia among social media users. Specifically, work published in the International Journal of Computational Science and Engineeringhas examined the behavior of Twitter users in their updates related to the COVID-19 pandemic to detect personality disorders related to paranoia.
Mourad Ellouze, Seifeddine Mechti, Moez Krichen and Lamia Hadrich Belguith of the University of Sfax in Tunisia and Vinayakumar Ravi of Prince Mohammad Bin Fahd University in Khobar, Saudi Arabia, suggest that people’s behavior towards the pandemic is driven by mistrust towards authority and fueled by disinformation has somewhat hindered the way we have dealt with this global crisis.
The team suggests that in parallel with this common behavior in some people, there is a more distressing response in those with severe mental health problems related to paranoia. Such circumstances, when faced with the existential anguish of a deadly pandemic, can lead to severe anxiety, grief and suicidal thoughts.
Ultimately, the analysis by the team of Twitter users discussing COVID-19 could allow them to identify people who may be suffering needlessly and may find themselves in a personal crisis. In other words, the tools they discuss can be used as a proxy diagnosis that allows qualified professionals to provide an appropriate intervention to patients with paranoia. Perhaps it could also be used to guide decisions by Twitter itself and its algorithms that lower the risk to its vulnerable users.
Mourad Ellouze et al, An in-depth learning approach to detecting the behavior of people with personality disorders towards COVID-19 from Twitter, International Journal of Computational Science and Engineering (2022). DOI: 10.1504/IJCSE.2022.124553
Quote: Detection of paranoia among social media users (2022, August 3), retrieved August 3, 2022 from https://phys.org/news/2022-08-paranoia-social-media-users.html
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