FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4205975063> ?p ?o ?g. }

• W4205975063 abstract "<sec> <title>BACKGROUND</title> Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. </sec> <sec> <title>OBJECTIVE</title> Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. </sec> <sec> <title>METHODS</title> The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. </sec> <sec> <title>RESULTS</title> We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. </sec> <sec> <title>CONCLUSIONS</title> This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process. </sec>" @default.
• W4205975063 created "2022-01-25" @default.
• W4205975063 creator A5011966862 @default.
• W4205975063 creator A5047524995 @default.
• W4205975063 creator A5084238830 @default.
• W4205975063 creator A5084522362 @default.
• W4205975063 date "2021-07-12" @default.
• W4205975063 modified "2023-09-29" @default.
• W4205975063 title "Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study (Preprint)" @default.
• W4205975063 cites W1741567663 @default.
• W4205975063 cites W1965979587 @default.
• W4205975063 cites W1993032259 @default.
• W4205975063 cites W2057104007 @default.
• W4205975063 cites W2066305013 @default.
• W4205975063 cites W2084518431 @default.
• W4205975063 cites W2095419287 @default.
• W4205975063 cites W2146029572 @default.
• W4205975063 cites W2152255971 @default.
• W4205975063 cites W2157700119 @default.
• W4205975063 cites W2171762906 @default.
• W4205975063 cites W2252849902 @default.
• W4205975063 cites W2261525379 @default.
• W4205975063 cites W2329142452 @default.
• W4205975063 cites W2460585769 @default.
• W4205975063 cites W2462044331 @default.
• W4205975063 cites W2535284022 @default.
• W4205975063 cites W2612560394 @default.
• W4205975063 cites W2752353918 @default.
• W4205975063 cites W2770259901 @default.
• W4205975063 cites W2783664002 @default.
• W4205975063 cites W2803549048 @default.
• W4205975063 cites W2803758083 @default.
• W4205975063 cites W2804387108 @default.
• W4205975063 cites W2884721711 @default.
• W4205975063 cites W2888501547 @default.
• W4205975063 cites W2891543977 @default.
• W4205975063 cites W2908395583 @default.
• W4205975063 cites W2917976736 @default.
• W4205975063 cites W2922770828 @default.
• W4205975063 cites W2954548380 @default.
• W4205975063 cites W2964131409 @default.
• W4205975063 cites W2977219551 @default.
• W4205975063 cites W3010883626 @default.
• W4205975063 cites W3011573535 @default.
• W4205975063 cites W3016994297 @default.
• W4205975063 cites W3020062806 @default.
• W4205975063 cites W3024556868 @default.
• W4205975063 cites W3039050487 @default.
• W4205975063 cites W3041481184 @default.
• W4205975063 cites W3045662600 @default.
• W4205975063 cites W3045850735 @default.
• W4205975063 cites W3047904982 @default.
• W4205975063 cites W3088320621 @default.
• W4205975063 cites W3089592695 @default.
• W4205975063 cites W3096451393 @default.
• W4205975063 cites W3112367250 @default.
• W4205975063 cites W3119397230 @default.
• W4205975063 cites W3121601851 @default.
• W4205975063 cites W3124593608 @default.
• W4205975063 cites W3125147044 @default.
• W4205975063 cites W3128282934 @default.
• W4205975063 cites W3133655860 @default.
• W4205975063 cites W3134173694 @default.
• W4205975063 cites W3134228136 @default.
• W4205975063 cites W3136677622 @default.
• W4205975063 cites W3138145311 @default.
• W4205975063 cites W3146405053 @default.
• W4205975063 cites W3148271110 @default.
• W4205975063 cites W3153731940 @default.
• W4205975063 cites W3161329378 @default.
• W4205975063 cites W3164443714 @default.
• W4205975063 cites W3165317333 @default.
• W4205975063 cites W4205952910 @default.
• W4205975063 cites W4231115877 @default.
• W4205975063 cites W4235873971 @default.
• W4205975063 cites W4236619680 @default.
• W4205975063 cites W4247239021 @default.
• W4205975063 cites W69738639 @default.
• W4205975063 doi "https://doi.org/10.2196/preprints.31983" @default.
• W4205975063 hasPublicationYear "2021" @default.
• W4205975063 type Work @default.
• W4205975063 citedByCount "0" @default.
• W4205975063 crossrefType "posted-content" @default.
• W4205975063 hasAuthorship W4205975063A5011966862 @default.
• W4205975063 hasAuthorship W4205975063A5047524995 @default.
• W4205975063 hasAuthorship W4205975063A5084238830 @default.
• W4205975063 hasAuthorship W4205975063A5084522362 @default.
• W4205975063 hasBestOaLocation W42059750632 @default.
• W4205975063 hasConcept C108827166 @default.
• W4205975063 hasConcept C136764020 @default.
• W4205975063 hasConcept C142724271 @default.
• W4205975063 hasConcept C159047783 @default.
• W4205975063 hasConcept C22070199 @default.
• W4205975063 hasConcept C2778431730 @default.
• W4205975063 hasConcept C2779134260 @default.
• W4205975063 hasConcept C2908647359 @default.
• W4205975063 hasConcept C3008058167 @default.
• W4205975063 hasConcept C41008148 @default.
• W4205975063 hasConcept C43169469 @default.
• W4205975063 hasConcept C518677369 @default.

• next