Researchers from Brunel University London and the University of Leicester have developed a novel artificial intelligence (AI) tool that accurately identifies depressed Twitter users by extracting and analyzing 38 data points from the public profile.
The algorithm considers factors like post contents, posting times, and the user’s social circle to determine whether the Twitterati is depressed.
Developers of this AI algorithm claim that it has a respectable accuracy of nearly 90%. Due to various causes, including social stigma or ignorance of mental state, a vast proportion of prospective depression sufferers around the world do not seek professional care.
This negligence then leads to severe delays in diagnosis and treatment. The new AI algorithm can considerably help change the scenario and might open new ways for future diagnosis.
Director of Brunel’s Institute of Digital Future, and co-author of the study, Abdul Sadka, said, “We tested the algorithm on two large databases and benchmarked our results against other depression detection techniques. In all cases, we’ve managed to outperform existing techniques in terms of their classification accuracy.”
The AI tool filters people with fewer than five tweets and then uses natural language processing to repair misspellings and abbreviations in the remaining profiles. According to the researchers, such technology might potentially detect a user’s depression before they publish something online. This would allow social media platforms to raise alerts that might help early diagnose and treat identified users.
Moreover, the bot can also be used for other purposes, including sentiment analysis, employee screening, criminal investigation, etc.
“The next stage of this research will be to examine its validity in different environments or backgrounds, and more importantly, the technology raised from this investigation may be further developed to other applications, such as e-commerce, recruitment examination or candidacy screening,” said co-author of the study Huiyu Zhou.
The study has been published in IEEE Transactions on Affective Computing.