Could an algorithm detect unhappiness in social media posts?
Image credit: Rawpixelimages | Dreamstime
Researchers in Spain have developed an algorithm that can detect unhappiness from the text and images users share on social networks.
We spend a substantial amount of our time sharing images, videos, or thoughts on social networks. Now, researchers at the Universitat Oberta de Catalunya (UOC) in Barcelona have developed an algorithm that aims to help psychologists diagnose possible mental health problems through the content people post on these platforms.
According to William Glasser's Choice Theory, there are five basic needs that are central to all human behaviour: Survival, Power, Freedom, Belonging and Fun. These needs even have an influence on the images we choose to upload to our Instagram page, the team said.
“How we present ourselves on social media can provide useful information about behaviours, personalities, perspectives, motives and needs,” explained Mohammad Mahdi Dehshibi, a researcher at the AI for Human Well-being (AIWELL) group at the UOC.
The research team has spent two years working on a deep-learning model that identifies the five needs described by Glasser, using multi-modal data such as images, text, biography and geolocation.
For the study, which has been published in the journal IEEE Transactions on Affective Computing, the researchers analysed 86 Instagram profiles, in both Spanish and Persian.
Drawing on neural networks and databases, the experts trained an algorithm to identify the content of the images and to categorise textual content by assigning different labels proposed by psychologists, who compared the results with a database containing over 30,000 images, captions and comments.
According to the research team, the experiments “show promising accuracy and complementary information between visual and textual cues”.
Glasser’s theory argues that each choice users make on social media does not respond to just one basic need – the multi-label approach of this study helps to debunk this.
Dehshibi uses an example to explain this: “Imagine that a cyclist is riding up a mountain, and at the top, they can choose between sharing a selfie and a group photo. If they choose the selfie, we perceive a need for Power, but if they choose the other option, we can conclude that the person is not only looking for Fun but also a way to satisfy their need for Belonging.”
The study also found that Spanish-speaking users are more likely to mention relationship problems when feeling depressed than English speakers.
Furthermore, the fact that the profiles belong to people who communicate in two different languages helped the researchers avoid cultural bias during their analysis.
“Studying data from social networks that belong to non-English-speaking users could help build inclusive and diverse tools and models for addressing mental health problems in people with diverse cultural or linguistic backgrounds,” they noted.
The research team believe that their study can help improve preventive measures, ranging from identification to improved treatment when a person has been diagnosed with a mental health disorder.
In November 2021, researchers at Brigham Young University (BYU), Johns Hopkins and Harvard created an algorithm that they say can predict suicidal thoughts and behaviour among adolescents with a 91 per cent accuracy.
Back in 2019, University of Vermont researchers developed an artificial-intelligence-based system that can detect signs of anxiety and depression in the speech patterns of young children.
Meanwhile, computer scientists from the University of Alberta, Canada have developed algorithms that can now, more accurately, detect and identify depression through vocal cues.
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