Entertainment programming in the United States has long addressed major public health issues. In the present study, we used a culture-centric approach to systematically investigate the role of television storylines in facilitating health-related conversations on social media. In particular, we examined Twitter conversations about sexual and police-involved trauma prompted by portrayals on the fictional television drama . Guided by the culture-centric model of narratives in health promotion, we classified the tweets (N = 1,671) into four main thematic categories: identification, social proliferation, emotions, and intentions. The analysis also revealed several subthemes, including identification with characters and cultural elements, expressions of pain and joy, information seeking and sharing, and the need to address intergenerational trauma and promote intergenerational conversations. The data suggests that Twitter may provide a vehicle for engaging in difficult conversations. We discuss the theoretical and practical implications of the study for mental health communication with Black Americans.
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http://dx.doi.org/10.1080/10410236.2021.1888454 | DOI Listing |
Front Res Metr Anal
January 2025
Centre for Postgraduate Studies, Cape Peninsula University of Technology, Cape Town, South Africa.
Big Data communication researchers have highlighted the need for qualitative analysis of online science conversations to better understand their meaning. However, a scholarly gap exists in exploring how qualitative methods can be applied to small data regarding micro-bloggers' communications about science articles. While social media attention assists with article dissemination, qualitative research into the associated microblogging practices remains limited.
View Article and Find Full Text PDFFront Genet
January 2025
Baylor College of Medicine, Center for Medical Ethics and Health Policy, Houston, TX, United States.
Social media sites like X (formerly Twitter) increasingly serve as spaces for the public to discuss controversial topics. Social media can spark extreme viewpoints and spread biased or inaccurate information while simultaneously allowing for debate around policy-relevant topics. The arrest of Joseph J.
View Article and Find Full Text PDFR Soc Open Sci
January 2025
Mathematics Application Consortium for Science and Industry (MACSI), University of Limerick, Limerick, Ireland.
The analysis of social networks enables the understanding of social interactions, polarization of ideas and the spread of information, and therefore plays an important role in society. We use Twitter data-as it is a popular venue for the expression of opinion and dissemination of information-to identify opposing sides of a debate and, importantly, to observe how information spreads between these groups in our current polarized climate. To achieve this, we collected over 688 000 tweets from the Irish Abortion Referendum of 2018 to build a conversation network from users' mentions with sentiment-based homophily.
View Article and Find Full Text PDFHealthcare (Basel)
December 2024
Division of Rheumatology, Mayo Clinic Florida, Jacksonville, FL 32224, USA.
Background And Objectives: Fibromyalgia has many unmet needs relating to treatment, and the delivery of effective and evidence-based healthcare is lacking. We analyzed social media conversations to understand the patients' perspectives on the lived experience of fibromyalgia, factors reported to trigger flares of pain, and the treatments being discussed, identifying barriers and opportunities to improve healthcare delivery.
Methods: A non-interventional retrospective analysis accessed detail-rich conversations about fibromyalgia patients' experiences with 714,000 documents, including a fibromyalgia language tag, which were curated between May 2019 and April 2021.
Front Public Health
December 2024
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Objective: To characterize the public conversations around long COVID, as expressed through X (formerly Twitter) posts from May 2020 to April 2023.
Methods: Using X as the data source, we extracted tweets containing #long-covid, #long_covid, or "long covid," posted from May 2020 to April 2023. We then conducted an unsupervised deep learning analysis using Bidirectional Encoder Representations from Transformers (BERT).
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