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Analysis of User Diversity-Based Patterns of Public Discourse on Twitter About Mental Health in the Context of Online Learning During COVID-19

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HCI International 2023 – Late Breaking Papers (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14056))

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Abstract

This work focuses on the analysis of user diversity-based patterns of the public discourse on Twitter about mental health in the context of online learning during COVID-19. Two aspects of user diversity – gender and location are the focus of this work. A dataset comprising 52,984 Tweets about online learning during COVID-19, posted on Twitter between November 9, 2021, and July 13, 2022, was used for this analysis. A Bag of Words model comprising 218 keywords related to mental health was developed and used to categorize the Tweets into two topics – Tweets that focused on mental health in the context of online learning during COVID-19 (Topic 1) and Tweets that did not focus on mental health in the context of online learning during COVID-19 (Topic 2). Thereafter, two algorithms were developed to infer the gender and location of a Twitter user based on their Twitter username and the location listed on their Twitter account, respectively. The results of this work present several novel findings. First, for Topic 1, a higher percentage of the Tweets were posted by females as compared to males. However, for Topic 2, a higher percentage of the Tweets were posted by males as compared to females. Second, Twitter users from 193 countries posted about Topic 1, and Twitter users from 228 countries posted about Topic 2. Third, the highest number of Tweets on Topic 1 and Topic 2 were posted by Twitter users from the United States. Finally, the work also reports the gender-based Tweeting patterns on Topic 1 and Topic 2 from different countries. For instance, in the United States, for Topic 1, a higher percentage of the Tweets were posted by females as compared to males. However, for Topic 2, males posted a higher percentage of Tweets as compared to females.

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  1. Department of Computer Science, Emory University, Atlanta, GA, 30322, USA

    Nirmalya Thakur, Hazel Cho, Haokun Cheng & Hyunji Lee

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  1. Nirmalya Thakur
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  1. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  2. Tokyo University of Science, Tokyo, Japan

    Yumi Asahi

  3. University of Bucharest, Bucharest, Romania

    Adela Coman

  4. University of Tsukuba, Tsukuba, Japan

    Simona Vasilache

  5. Eindhoven University of Technology, Eindhoven, The Netherlands

    Matthias Rauterberg

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Thakur, N., Cho, H., Cheng, H., Lee, H. (2023). Analysis of User Diversity-Based Patterns of Public Discourse on Twitter About Mental Health in the Context of Online Learning During COVID-19. In: Mori, H., Asahi, Y., Coman, A., Vasilache, S., Rauterberg, M. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14056. Springer, Cham. https://doi.org/10.1007/978-3-031-48044-7_27

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