Social Media and Healthcare Technology
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ItemUnderstanding telemedicine service users’ perceptions: A text mining analysis on social media discussion( 2023-01-03)Telemedicine has drawn noticeable attention due to the advancement of information technology, and it saw a surge in popularity during the COVID-19 pandemic. This study is aimed at understanding telemedicine users’ perceptions on their care services, as well as identifying the aspects of telemedicine that can be improved to enhance users’ experience and satisfaction. Specifically, we utilized a topic modeling approach with Latent Dirichlet Allocation (LDA) to analyze telemedicine-related discussion posts on Reddit to discover the topics and themes that telemedicine service users are interested in, as well as the perceptions that users have of those topics and themes. 11 topics and 6 themes were discovered by the LDA algorithm. Lastly, we provide our suggestions and insights on how telemedicine services and practitioners can implement the themes, as well as directions for future study.
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ItemInfrastructural and network support in the illness experience: The role of community crowdsourcing in self-care( 2023-01-03)Social sharing within pseudonymous online communities can assist- or hinder- the self-management of health care and emphasize challenges associated with chronic illness. For those who are affected by chronic illness who participate in a public online community, the breadth of topics discussed in a corpus of a pseudonymous group can lend insight into tracking the interaction processes and outcomes. Examining the topics discussed in the endometriosis subreddit (r/endo) informs public health strategies as well as ethical considerations in health care surrounding stigmatized illness in a public community. In the present study, the data corpus of r/endo was analyzed and scraped, employing computational data mining techniques to uncover the discursive practices within the community. The topics of self-management is constructed by diagnosed and undiagnosed patients; endometriosis etiology and understanding symptoms negotiated within the community. In an online community for those who face unique health challenges from endometriosis, we argue that users engage in a form of community crowdsourcing via information exchange and network support, spurred by the platform affordances of Reddit.
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ItemQuality Indicators of Online Cancer Communities from the Perspective of Cancer-Affected People( 2023-01-03)Online cancer communities established by non-profit cancer organisations help cancer-affected people seeking information and facing emotional and psychological challenges to communicate and interact with a network of people suffering from similar issues. However, the quality features of online cancer communities from the perception of users of the information are under-researched. Using the factors credibility, content, and design, this study adopted a multi-theory perspective by integrating the Information Quality (IQ) assessment framework, the source credibility model, and the two-factor theory for website design to develop a framework to examine the quality indicators of online cancer communities. Data collected via semi-structured interviews with 14 participants showed that dimensions underlying content and credibility factors are crucial for using the online community. All dimensions underlying the design factor were essential factors, except for the visualisation of the website and privacy and data protection that constituted motivating factors for using the online community.
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ItemEarly Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features( 2023-01-03)Clinical depression is a serious mental disorder that poses challenges for both personal and public health. Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model’s interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies.
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ItemUsing Twitter Post Data to Ascertain the Sentiment of Alcohol-related Blackouts in the United States( 2023-01-03)Research shows variability in how alcohol-related blackouts (periods of memory loss during/after drinking) are subjectively evaluated. We accessed 3.5 million original Tweets written in the U.S. between July 2009 and February 2020 that referenced blackouts, and coded the sentiment (positive or negative) of those Tweets, using the machine learning function of a Twitter-sponsored commercial platform. The sentiment of Tweets was examined by day of week and compared to the sentiment of blackout Tweets on certain holidays to non-celebration matched days. Tweets were more likely to have a positive (73%) than negative sentiment, and positive Tweets were more common during weekends. Relative to typical non-celebratory weekends, a greater proportion of blackout Tweets were positive around Thanksgiving and New Year’s Eve, though differences were not observed relative to several other celebratory periods (e.g., Superbowl). Results have implications for online interventions, which can use social networking sites to target alcohol during high-risk periods.
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ItemDeciphering Medical Errors: What Matters for Patients on Social Media( 2023-01-03)This study investigates medical errors, germane to patient safety, from the patient’s perspective. We analyzed social media data, Twitter posts, about patients’ perspective on their medical experiences, which have been rarely translated into a systemic and rigorous research result. Employing a combined-research method, the qualitative content analysis and the analytical automatic categorization of text data, we analyzed 1,806 tweet entries during four and half years, from December 2017 to June 2022. We identified the categories and consequences of medical errors, critical from the patient’s perspective. The common medical errors include ignorance, misdiagnosis, negligence, and medication errors. The manifested consequences of medical errors include medical complications, death, and paralyzed/disabled. The study emphasizes the importance of patient’s experience in complementing other error reporting systems and mechanisms, that have been utilized by healthcare professionals for establishing more meaningful recommendations for reducing medical errors.