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.

ATMTN: a telemammography network architecture.

One of the goals of the National Cancer Institute (NCI) to reach more than 80% of eligible women in mammography screening by the year 2000 yet remains as a challenge. In fact, a recent medical report reveals that while other types of cancer are experiencing negative growth, breast cancer has been the only one with a positive growth rate over the last few years. This is primarily due to the fact that 1) examination process is a complex and lengthy one and 2) it is not available to the majority of women who live in remote sites. Currently for mammography screening, women have to go to doctors or cancer centers/hospitals annually while high-risk patients may have to visit more often. One way to resolve these problems is by the use of advanced networking technologies and signal processing algorithms. On one hand, software modules can help detect, with high precision, true negatives (TN), while marking true positives (TP) for further investigation. Unavoidably, in this process some false negatives (FN) will be generated that are potentially life threatening; however, inclusion of the detection software improves the TP detection and, hence, reduces FNs drastically. Since TNs are the majority of examinations on a randomly selected population, this first step reduces the load on radiologists by a tremendous amount. On the other hand, high-speed networking equipment can accelerate the required clinic-lab connection and make detection, segmentation, and image enhancement algorithms readily available to the radiologists. This will bring the breast cancer care, caregiver, and the facilities to the patients and expand diagnostics and treatment to the remote sites. This research describes asynchronous transfer mode telemammography network (ATMTN) architecture for real-time, online screening, detection and diagnosis of breast cancer. ATMTN is a unique high-speed network integrated with automatic robust computer-assisted diagnosis-detection/digital signal processing (CAD/DSP) methods for mass detection, region of interest (ROI) compression algorithms using Digital Imaging and Communications in Medicine (DICOM) 3.0 medical image standard. While ATMTN has the advantage of higher penetration for cancer screening, it provides the diagnosis with higher efficiency, better accuracy and potentially lower cost. This paper presents the development of the infrastructure and algorithm design for ATMTN-based telemammography. The research goals involved: 1) networking stations for telemammography to demonstrate, evaluate, and validate technologies and methods for delivering mammography screening services via high-speed (155 MB/s) links, performing real-time network-transmitted, high-resolution mammograms for immediate diagnosis as a "second opinion" strategy; 2) development of object-oriented compression methods for storage, retrieval and transmission of mammograms; 3) inclusion and optimization of detection algorithms for identification of normal images in different resolutions to increase the speed and effectiveness of telemammography as a "second opinion" strategy; 4) resolving the compatibility issues between images from different equipment (DICOM standards); and 5) optimization of an integrated ATMTN with adaptive CAD/DSP methods that are robust for large image databases and input sources.