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      Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis

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          Abstract

          Objective

          The objective of this study is to understand the primary topics of consumer discussion on Twitter associated with telehealth for mental health or substance abuse for prepandemic versus during-pandemic time-periods, using a state-of-the-art machine learning (ML) natural language processing (NLP) method.

          Materials and Methods

          The primary methodological phases of this project were: (1) collecting, cleaning, and filtering data (tweets) from January 2014 to June 2021, (2) describing the final corpus, (3) running and optimizing Bidirectional Encoder Representations from Transformers (BERT; using BERTopic in Python) models, and (4) human refinement of topic model results and thematic classification of topics.

          Results

          The number of tweets in this context increased by 4 times during the pandemic (2017 tweets prepandemic vs 8672 tweets during the pandemic). During the pandemic topics were more frequently mental health related than substance abuse related. Top during-pandemic topics were therapy, suicide, pain (associated with burnout and drinking), and mental health diagnoses such as ADHD and autism. Anxiety was a key topic of discussion both pre- and during the pandemic.

          Discussion

          Telehealth for mental health and substance abuse is being discussed more frequently online, which implies growing demand. Given the topics extracted as proxies for demand, the most demand is currently for telehealth for mental health primarily, especially for children, parents, and therapy for those with anxiety or depression, and substance abuse secondarily.

          Conclusions

          Scarce telehealth resources can be allocated more efficiently if topics of consumer discussion are included in resource allocation decision- and policy-making processes.

          Lay Summary

          Telehealth for mental health and substance abuse is being discussed more frequently online. To determine what aspects of telehealth for mental health and/or substance abuse were being discussed most on Twitter, both before the pandemic and during the pandemic, we downloaded relevant tweets and ran a specialized machine learning model that extracts the most popular keywords from tweets as well as combines similar keywords into overall topics. We find 33 relevant topics prepandemic and 32 relevant topics during the pandemic to be relevant in this context. Given the topics extracted as proxies for demand, the most demand is currently for telehealth for mental health primarily, especially for children, parents, and therapy for those with anxiety or depression, and substance abuse secondarily. We also find that therapy and therapists were the top areas of discussion in regard to telehealth for mental health and/or substance abuse during the pandemic. These results can be applied to telehealth decision-making processes. In particular, scarce telehealth resources can be allocated more efficiently, particularly to those who currently need or want them most, if topics of consumer discussion are included in resource allocation decision- and policy-making processes.

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          Most cited references38

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          The measurement of observer agreement for categorical data.

          This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.
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            Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study

            Background COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. Objective The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. Methods Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. Results The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. Conclusions Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.
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              Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach

              Background It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring. Objective The objective of this study is to examine COVID-19–related discussions, concerns, and sentiments using tweets posted by Twitter users. Methods We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, “coronavirus,” “COVID-19,” “quarantine”) from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. Results Popular unigrams included “virus,” “lockdown,” and “quarantine.” Popular bigrams included “COVID-19,” “stay home,” “corona virus,” “social distancing,” and “new cases.” We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. Conclusions This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.
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                Author and article information

                Journal
                JAMIA Open
                JAMIA Open
                jamiaoa
                JAMIA Open
                Oxford University Press
                2574-2531
                July 2022
                27 April 2022
                27 April 2022
                : 5
                : 2
                : ooac028
                Affiliations
                [1 ]Institute of Health Administration, Georgia State University , Atlanta, Georgia, USA
                [2 ]Department of Computer Information Systems, Robinson College of Business, Georgia State University , Atlanta, Georgia, USA
                [3 ]Institute for Insight, Robinson College of Business, Georgia State University , Atlanta, Georgia, USA
                Author notes
                Corresponding Author: Aaron Baird, PhD, Institute of Health Administration, Robinson College of Business, Georgia State University, 35 Broad Street, Atlanta, GA 30303, USA; abaird@ 123456gsu.edu
                Author information
                https://orcid.org/0000-0002-5620-2926
                Article
                ooac028
                10.1093/jamiaopen/ooac028
                9047171
                35495736
                24d07073-03f7-4fba-bc9c-5d828c0b0e6a
                © The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 20 January 2022
                : 18 March 2022
                : 13 April 2022
                : 14 April 2022
                Page count
                Pages: 8
                Categories
                Research and Applications
                AcademicSubjects/SCI01530
                AcademicSubjects/MED00010
                AcademicSubjects/SCI01060

                telehealth,mental health,substance abuse,machine learning,bert (bertopic),social media (twitter),pandemic

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