Abstract：
BACKGROUND: 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.
OBJECTIVE: This study aims to demonstrate the feasibility of language on private social media as an indicator of crisis-induced disruption to mental well-being.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
OBJECTIVE: This study aims to demonstrate the feasibility of language on private social media as an indicator of crisis-induced disruption to mental well-being.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：像COVID-19这样的大规模危机事件通常会对个体的心理健康产生次要影响。大学生特别容易受到这种影响。传统的基于调查的方法来识别需要支持的人不会在大量人口中扩展，也无法提供及时的见解。我们通过社交媒体数据和机器学习寻求替代方法。我们的模型旨在补充调查，并提供早期，精确，以及对COVID-19干扰的学生的客观预测。
目的：本研究旨在证明在私人社交媒体上使用语言作为危机引起的心理健康中断指标的可行性。
方法：我们模拟了由43名本科生提供的4124个Facebook帖子，跨越超过2年。我们提取了他们帖子和评论的心理语言属性的时间趋势。这些趋势被用作预测COVID-19如何破坏他们的心理健康的特征。
结果：支持社交媒体的模型的F1评分为0.79，比根据参与者自我报告的精神状态训练的模型提高了39%。我们使用的功能在预测其他精神状态方面显示出希望，例如焦虑，抑郁症,社会,隔离,和自杀行为（F1评分在0.85和0.93之间变化）。我们还发现，选择COVID-19诱导的封锁后7个月的时间窗口效果更好，因此,为数据最小化铺平道路。
结论：我们通过开发一种在私人社交媒体上利用语言的机器学习模型，预测了COVID-19对心理健康的破坏。这些帖子中的语言描述了学生在线行为中的心理语言趋势。这些纵向趋势比在相关心理健康问卷上训练的模型更好地帮助预测心理健康中断。我们的工作激发了对早期潜在应用的进一步研究，精确，并在危机时期自动向关心自己心理健康的人发出警告。
目的：本研究旨在证明在私人社交媒体上使用语言作为危机引起的心理健康中断指标的可行性。
方法：我们模拟了由43名本科生提供的4124个Facebook帖子，跨越超过2年。我们提取了他们帖子和评论的心理语言属性的时间趋势。这些趋势被用作预测COVID-19如何破坏他们的心理健康的特征。
结果：支持社交媒体的模型的F1评分为0.79，比根据参与者自我报告的精神状态训练的模型提高了39%。我们使用的功能在预测其他精神状态方面显示出希望，例如焦虑，抑郁症,社会,隔离,和自杀行为（F1评分在0.85和0.93之间变化）。我们还发现，选择COVID-19诱导的封锁后7个月的时间窗口效果更好，因此,为数据最小化铺平道路。
结论：我们通过开发一种在私人社交媒体上利用语言的机器学习模型，预测了COVID-19对心理健康的破坏。这些帖子中的语言描述了学生在线行为中的心理语言趋势。这些纵向趋势比在相关心理健康问卷上训练的模型更好地帮助预测心理健康中断。我们的工作激发了对早期潜在应用的进一步研究，精确，并在危机时期自动向关心自己心理健康的人发出警告。
