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Tracking Public Depression from Tweets on COVID-19 and Its Comparison with Pre-pandemic Time

Social media especially the Twitter platform has become a good data-source in Japan for tracking various social issues including depression and other mental health problems. It can overcome the under-representation and sampling bias of the survey data. In this study, we develop a machine learning approach to predict depression of Japanese people and compare their depression levels between pre-pandemic (2018) and pandemic (2020) times. We use three datasets in this study in which the first dataset is used for model development and its validation, while the rest two are used as test datasets for depression prediction. These two datasets represent timeseries tweets for the years 2018 (pre-pandemic) and 2020 (pandemic), respectively. After preprocessing the tweets, the Bag-of-words (BOW) feature is computed for each test dataset, which is later fed to the trained Logistic Regression (LOGR) model to classify tweets into "Depressive" and "Non-Depressive" categories. An analysis on the classified tweets shows a significant increase of depressive tweets in 2020, when compared with those in 2018. The covid related depressive tweets was found 50.37% of the total covid-related tweets and 8.6% of the total depressive tweets in the 2020 dataset, which indicates an increased impact of depression on the Japanese people due to COVID-19. Also, the peak depression occurs in June and August 2020 just after the first peak of the death progression timeseries in Japan, which indicates the consequences or shocks of exponential death-turmoil along with the increasing economic uncertainty and mobility restrictions. The timely application of our method to suitable textual datasets can minimize the calamity of future disasters like COVID-19 as well as it can help making suitable policy decisions for sustainable solutions against depression.