Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313244989> ?p ?o ?g. }
- W4313244989 endingPage "354" @default.
- W4313244989 startingPage "354" @default.
- W4313244989 abstract "Understanding local public attitudes toward receiving vaccines is vital to successful vaccine campaigns. Social media platforms may help uncover vaccine sentiments during infectious disease outbreaks at the local level, and whether offline local events support vaccine-promotion efforts. Communication Infrastructure Theory (CIT) served as a guiding framework for this case study of the San Diego region examining local public sentiment toward vaccines expressed on Twitter during the COVID-19 pandemic. We performed a sentiment analysis (including positivity and subjectivity) of 187,349 tweets gathered from May 2020 to March 2021, and examined how sentiment corresponded with local vaccine deployment. The months of November and December (52.9%) 2020 saw a majority of tweets expressing positive sentiment and coincided with announcements of offline local events signaling San Diego's imminent deployment of COVID-19 vaccines. Across all months, tweets remained mostly objective (never falling below 63%). In terms of CIT, considering multiple levels of the Story Telling Network in online spaces, and examining sentiment about vaccines on Twitter may help scholars to explore the Communication Action Context, as well as cultivate positive community attitudes to improve the Field of Health Action regarding vaccines. Real-time analysis of local tweets during development and deployment of new vaccines may help monitor local public responses and guide promotion of immunizations in communities." @default.
- W4313244989 created "2023-01-06" @default.
- W4313244989 creator A5020396630 @default.
- W4313244989 creator A5021407370 @default.
- W4313244989 creator A5054110567 @default.
- W4313244989 creator A5054993094 @default.
- W4313244989 creator A5067299846 @default.
- W4313244989 creator A5071121626 @default.
- W4313244989 date "2022-12-26" @default.
- W4313244989 modified "2023-10-18" @default.
- W4313244989 title "Examining Vaccine Sentiment on Twitter and Local Vaccine Deployment during the COVID-19 Pandemic" @default.
- W4313244989 cites W1506474661 @default.
- W4313244989 cites W1963529192 @default.
- W4313244989 cites W1988023854 @default.
- W4313244989 cites W2011587443 @default.
- W4313244989 cites W2037059617 @default.
- W4313244989 cites W2042978973 @default.
- W4313244989 cites W2052263399 @default.
- W4313244989 cites W2057130758 @default.
- W4313244989 cites W2058759764 @default.
- W4313244989 cites W2063788260 @default.
- W4313244989 cites W2066890996 @default.
- W4313244989 cites W2075088218 @default.
- W4313244989 cites W2084518431 @default.
- W4313244989 cites W2089883681 @default.
- W4313244989 cites W2093588201 @default.
- W4313244989 cites W2099083748 @default.
- W4313244989 cites W2103867833 @default.
- W4313244989 cites W2113675788 @default.
- W4313244989 cites W2113677323 @default.
- W4313244989 cites W2114463823 @default.
- W4313244989 cites W2115023510 @default.
- W4313244989 cites W2118943589 @default.
- W4313244989 cites W2148506018 @default.
- W4313244989 cites W2329734935 @default.
- W4313244989 cites W2411777166 @default.
- W4313244989 cites W2582561810 @default.
- W4313244989 cites W2763290093 @default.
- W4313244989 cites W2770259901 @default.
- W4313244989 cites W2790944719 @default.
- W4313244989 cites W2808537791 @default.
- W4313244989 cites W2901175196 @default.
- W4313244989 cites W2903740044 @default.
- W4313244989 cites W2904592940 @default.
- W4313244989 cites W2941531095 @default.
- W4313244989 cites W2952995565 @default.
- W4313244989 cites W2981753308 @default.
- W4313244989 cites W2999130971 @default.
- W4313244989 cites W3015216529 @default.
- W4313244989 cites W3024685896 @default.
- W4313244989 cites W3029831279 @default.
- W4313244989 cites W3033165670 @default.
- W4313244989 cites W3039410321 @default.
- W4313244989 cites W3048424114 @default.
- W4313244989 cites W3082954497 @default.
- W4313244989 cites W3084586512 @default.
- W4313244989 cites W3094030488 @default.
- W4313244989 cites W3107746014 @default.
- W4313244989 cites W3111430045 @default.
- W4313244989 cites W3116298071 @default.
- W4313244989 cites W3117177153 @default.
- W4313244989 cites W3117468040 @default.
- W4313244989 cites W3119813001 @default.
- W4313244989 cites W3120764866 @default.
- W4313244989 cites W3122609186 @default.
- W4313244989 cites W3122929523 @default.
- W4313244989 cites W3126978097 @default.
- W4313244989 cites W3127941442 @default.
- W4313244989 cites W3130770919 @default.
- W4313244989 cites W3139172628 @default.
- W4313244989 cites W3139329320 @default.
- W4313244989 cites W3148381663 @default.
- W4313244989 cites W3152680339 @default.
- W4313244989 cites W3155470693 @default.
- W4313244989 cites W3162753563 @default.
- W4313244989 cites W3165710938 @default.
- W4313244989 cites W3167620115 @default.
- W4313244989 cites W3172535605 @default.
- W4313244989 cites W3172902757 @default.
- W4313244989 cites W3195627052 @default.
- W4313244989 cites W4205184193 @default.
- W4313244989 cites W4206711400 @default.
- W4313244989 cites W4220684209 @default.
- W4313244989 cites W4232647949 @default.
- W4313244989 cites W4254395329 @default.
- W4313244989 doi "https://doi.org/10.3390/ijerph20010354" @default.
- W4313244989 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36612674" @default.
- W4313244989 hasPublicationYear "2022" @default.
- W4313244989 type Work @default.
- W4313244989 citedByCount "0" @default.
- W4313244989 crossrefType "journal-article" @default.
- W4313244989 hasAuthorship W4313244989A5020396630 @default.
- W4313244989 hasAuthorship W4313244989A5021407370 @default.
- W4313244989 hasAuthorship W4313244989A5054110567 @default.
- W4313244989 hasAuthorship W4313244989A5054993094 @default.
- W4313244989 hasAuthorship W4313244989A5067299846 @default.
- W4313244989 hasAuthorship W4313244989A5071121626 @default.
- W4313244989 hasBestOaLocation W43132449891 @default.