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Using Gradient Methods to Predict Twitter Users' Mental Health with Both COVID-19 Growth Patterns and Tweets

机译:使用渐变方法通过COVID-19增长模式和推文预测Twitter用户的心理健康

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Twitter users post tweets to express their feelings, emotions, and behavior. During COVID-19 times, people moved to varied life routines. Such a change in daily life affected people's mental health. We studied the mental health of twitter users during this time through their tweets and compared them with the COVID-19 growth pattern. We also attempted to forecast the depressive tweets and compared them with real data using ARIMA methods. We found our observations of tweets and COVID-19 Epidemic reports of WHO followed a similar pattern. Our forecast findings with ARIMA methods supported the real data.
机译:Twitter用户发布推文来表达他们的感受,情感和行为。在COVID-19期间,人们转向了各种各样的生活。日常生活中的这种变化影响了人们的心理健康。我们通过其推文研究了Twitter用户在这段时间内的心理健康状况,并将其与COVID-19增长模式进行了比较。我们还尝试预测令人沮丧的推文,并使用ARIMA方法将其与真实数据进行比较。我们发现我们对推文的观察和WHO的COVID-19流行病报告都遵循类似的模式。我们使用ARIMA方法的预测结果支持了真实数据。

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