Abstract
COVID-19 pandemic has adversely and disproportionately impacted people suffering from mental health issues and substance use problems. This has been exacerbated by social isolation during the pandemic and the social stigma associated with mental health and substance use disorders, making people reluctant to share their struggles and seek help. Due to the anonymity and privacy they provide, social media emerged as a convenient medium for people to share their experiences about their day to day struggles. Reddit is a well-recognized social media platform that provides focused and structured forums called subreddits, that users subscribe to and discuss their experiences with others. Temporal assessment of the topical correlation between social media postings about mental health/substance use and postings about Coronavirus is crucial to better understand public sentiment on the pandemic and its evolving impact, especially related to vulnerable populations. In this study, we conduct a longitudinal topical analysis of postings between subreddits r/depression, r/Anxiety, r/SuicideWatch, and r/Coronavirus, and postings between subreddits r/opiates, r/OpiatesRecovery, r/addiction, and r/Coronavirus from January 2020–October 2020. Our results show a high topical correlation between postings in r/depression and r/Coronavirus in September 2020. Further, the topical correlation between postings on substance use disorders and Coronavirus fluctuates, showing the highest correlation in August 2020. By monitoring these trends from platforms such as Reddit, epidemiologists, and mental health professionals can gain insights into the challenges faced by communities for targeted interventions.
This work was supported by the NIH under grant NIH R01AT010413-03S1.
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References
Asmundson, G.J., Paluszek, M.M., Landry, C.A., Rachor, G.S., McKay, D., Taylor, S.: Do pre-existing anxiety-related and mood disorders differentially impact covid-19 stress responses and coping? J. Anxiety Disord. 74, 102271 (2020)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Cameron, D., Smith, G.A., Daniulaityte, R., Sheth, A.P., Dave, D., Chen, L., Anand, G., Carlson, R., Watkins, K.Z., Falck, R.: Predose: a semantic web platform for drug abuse epidemiology using social media. J. Biomed. Inf. 46(6), 985–997 (2013)
Czeisler, M.É., Lane, R.I., Petrosky, E., Wiley, J.F., Christensen, A., Njai, R., Weaver, M.D., Robbins, R., Facer-Childs, E.R., Barger, L.K., et al.: Mental health, substance use, and suicidal ideation during the covid-19 pandemic-united states, June 24–30 2020. Morb. Mortal. Wkly Rep. 69(32), 1049 (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Ettman, C.K., Abdalla, S.M., Cohen, G.H., Sampson, L., Vivier, P.M., Galea, S.: Prevalence of depression symptoms in us adults before and during the Covid-19 pandemic. JAMA Netw. Open 3(9), e2019686–e2019686 (2020)
Gaur, M., et al.: “Let me tell you about your mental health!” contextualized classification of reddit posts to DSM-5 for web-based intervention. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753–762 (2018)
Medford, R.J., Saleh, S.N., Sumarsono, A., Perl, T.M., Lehmann, C.U.: An “infodemic”: leveraging high-volume twitter data to understand public sentiment for the COVID-19 outbreak
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Sands, P., Mundaca-Shah, C., Dzau, V.J.: The neglected dimension of global security-a framework for countering infectious-disease crises. N. Engl. J. Med. 374(13), 1281–1287 (2016)
Stokes, D.C., Andy, A., Guntuku, S.C., Ungar, L.H., Merchant, R.M.: Public priorities and concerns regarding covid-19 in an online discussion forum: longitudinal topic modeling. J. Gen. Intern. Med. 1 (2020)
Yazdavar, A.H., et al.: Semi-supervised approach to monitoring clinical depressive symptoms in social media. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 1191–1198 (2017)
Yin, H., Yang, S., Li, J.: Detecting topic and sentiment dynamics due to COVID-19 pandemic using social media, July 2020
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Alambo, A., Padhee, S., Banerjee, T., Thirunarayan, K. (2021). COVID-19 and Mental Health/Substance Use Disorders on Reddit: A Longitudinal Study. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12662. Springer, Cham. https://doi.org/10.1007/978-3-030-68790-8_2
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DOI: https://doi.org/10.1007/978-3-030-68790-8_2
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