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A thematic analysis of UK COVID-19 vaccine hesitancy discussions on Twitter.
BMC Public Health
January 2025
Department of Applied Health Sciences and Institute for Mental Health, University of Birmingham, Birmingham, UK.
Background: Following UK approval of the Pfizer-BioNTech and Oxford-AstraZeneca vaccines on 2/12/20 and 30/12/20 respectively, discussions about them emerged on the social media platform Twitter, (now 'X'). Previous research has shown that Twitter/ X is used by the UK public to engage with public health announcements and that social media influences public opinions of vaccines, including COVID-19 vaccines, globally. This study explored discussions on Twitter posted in response to the UK government's posts introducing the Pfizer-BioNTech and Oxford-AstraZeneca vaccines.
View Article and Find Full Text PDFAn Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach.
JMIR Form Res
December 2024
School of Media and Journalism, Kent State University, Kent, OH, United States.
Background: The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics.
View Article and Find Full Text PDFEntity Linking for real-time geolocation of natural disasters from social network posts.
PLoS One
October 2024
LASTIG, Univ Gustave Eiffel, IGN-ENSG, Champs-sur-Marne, France.
Article Synopsis
- Crisis managers need to rapidly assess natural disasters using "big picture" maps, and social networks play a key role in situational awareness.
- The focus of this study is on extracting and geolocating information from French Twitter posts related to natural disasters, using a specially developed dataset.
- The resulting Entity-Linking pipeline successfully performs in real-time and shows strong coherence in geolocation of entities, making it a valuable tool for crisis management.
Predicting public mental health needs in a crisis using social media indicators: a Singapore big data study.
Sci Rep
October 2024
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore.
Mental health issues have increased substantially since the onset of the COVID-19 pandemic. However, health policymakers do not have adequate data and tools to predict mental health demand, especially amid a crisis. Using time-series data collected in Singapore, this study examines if and how algorithmically measured emotion indicators from Twitter posts can help forecast emergency mental health needs.
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