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

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.