%0 Journal Article
%T Leveraging Social Media to Predict COVID-19-Induced Disruptions to Mental Well-Being Among University Students: Modeling Study.
%A Das Swain V
%A Ye J
%A Ramesh SK
%A Mondal A
%A Abowd GD
%A De Choudhury M
%J JMIR Form Res
%V 8
%N 0
%D 2024 Jun 25
%M 38916951
暂无%R 10.2196/52316
%X <B>BACKGROUND: </B>Large-scale crisis events such as COVID-19 often have secondary impacts on individuals' mental well-being. University students are particularly vulnerable to such impacts. Traditional survey-based methods to identify those in need of support do not scale over large populations and they do not provide timely insights. We pursue an alternative approach through social media data and machine learning. Our models aim to complement surveys and provide early, precise, and objective predictions of students disrupted by COVID-19.<BR><B>OBJECTIVE: </B>This study aims to demonstrate the feasibility of language on private social media as an indicator of crisis-induced disruption to mental well-being.<BR><B>METHODS: </B>We modeled 4124 Facebook posts provided by 43 undergraduate students, spanning over 2 years. We extracted temporal trends in the psycholinguistic attributes of their posts and comments. These trends were used as features to predict how COVID-19 disrupted their mental well-being.<BR><B>RESULTS: </B>The social media-enabled model had an F1-score of 0.79, which was a 39% improvement over a model trained on the self-reported mental state of the participant. The features we used showed promise in predicting other mental states such as anxiety, depression, social, isolation, and suicidal behavior (F1-scores varied between 0.85 and 0.93). We also found that selecting the windows of time 7 months after the COVID-19-induced lockdown presented better results, therefore, paving the way for data minimization.<BR><B>CONCLUSIONS: </B>We predicted COVID-19-induced disruptions to mental well-being by developing a machine learning model that leveraged language on private social media. The language in these posts described psycholinguistic trends in students' online behavior. These longitudinal trends helped predict mental well-being disruption better than models trained on correlated mental health questionnaires. Our work inspires further research into the potential applications of early, precise, and automatic warnings for individuals concerned about their mental health in times of crisis.