Paving the way for COVID survivors' psychosocial rehabilitation: Mining topics, sentiments, and their trajectories over time from Reddit.

Objective: This study examined major themes and sentiments and their trajectories and interactions over time using subcategories of Reddit data. The aim was to facilitate decision-making for psychosocial rehabilitation. Materials and Methods: We utilized natural language processing techniques, including topic modeling and sentiment analysis, on a dataset consisting of more than 38,000 topics, comments, and posts collected from a subreddit dedicated to the experiences of people who tested positive for COVID-19. In this longitudinal exploratory analysis, we studied the dynamics between the most dominant topics and subjects' emotional states over an 18-month period. Results: Our findings highlight the evolution of the textual and sentimental status of major topics discussed by COVID survivors over an extended period of time during the pandemic. We particularly studied pre- and post-vaccination eras as a turning point in the timeline of the pandemic. The results show that not only does the relevance of topics change over time, but the emotions attached to them also vary. Major social events, such as the administration of vaccines or enforcement of nationwide policies, are also reflected through the discussions and inquiries of social media users. In particular, the emotional state (i.e., sentiments and polarity of their feelings) of those who have experienced COVID personally. Discussion: Cumulative societal knowledge regarding the COVID-19 pandemic impacts the patterns with which people discuss their experiences, concerns, and opinions. The subjects' emotional state with respect to different topics was also impacted by extraneous factors and events, such as vaccination. Conclusion: By mining major topics, sentiments, and trajectories demonstrated in COVID-19 survivors' interactions on Reddit, this study contributes to the emerging body of scholarship on COVID-19 survivors' mental health outcomes, providing insights into the design of mental health support and rehabilitation services for COVID-19 survivors.

Examining COVID-19 vaccine attitude using SEM-Artificial Neural Networks approach: a case from Reddit community.

As new coronavirus variants continue to emerge, in order to better address vaccine-related concerns and promote vaccine uptake in the next few years, the role played by online communities in shaping individuals' vaccine attitudes has become an important lesson for public health practitioners and policymakers to learn. Examining the mechanism that underpins the impact of participating in online communities on the attitude toward COVID-19 vaccines, this study adopted a two-stage hybrid structural equation modeling (SEM)-artificial neural networks (ANN) approach to analyze the survey responses from 1037 Reddit community members. Findings from SEM demonstrated that in leading up to positive COVID-19 vaccine attitudes, sense of online community mediates the positive effects of perceived emotional support and social media usage, and perceived social norm mediates the positive effect of sense of online community as well as the negative effect of political conservatism. Health self-efficacy plays a moderating role between perceived emotional support and perceived social norm of COVID-19 vaccination. Results from the ANN model showed that online community members' perceived social norm of COVID-19 vaccination acts as the most important predictor of positive COVID-19 vaccine attitudes. This study highlights the importance of harnessing online communities in designing COVID-related public health interventions and accelerating normative change in relation to vaccination.