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dc.contributor.authorHendriks, S.
dc.contributor.authorDuking, P.
dc.contributor.authorMellalieu, Stephen D.
dc.date.accessioned2018-02-26T11:49:20Z
dc.date.available2018-02-26T11:49:20Z
dc.date.issued2016-09-01
dc.identifier.citationHendricks, S., Düking, P. and Mellalieu, S.D. (2016) 'Twitter strategies for web-based surveying: descriptive analysis from the International Concussion Study', JMIR Research Protocols, 5(3).en_US
dc.identifier.issn1929-0748
dc.identifier.urihttp://hdl.handle.net/10369/9302
dc.descriptionThis article was published in JMIR Research Protocols available at http://doi.org/10.2196/resprot.4542en_US
dc.description.abstractBACKGROUND: Social media provides researchers with an efficient means to reach and engage with a large and diverse audience. Twitter allows for the virtual social interaction among a network of users that enables researchers to recruit and administer surveys using snowball sampling. Although using Twitter to administer surveys for research is not new, strategies to improve response rates are yet to be reported. OBJECTIVE: To compare the potential and actual reach of 2 Twitter accounts that administered a Web-based concussion survey to rugby players and trainers using 2 distinct Twitter-targeting strategies. Furthermore, the study sought to determine the likelihood of receiving a retweet based on the time of the day and day of the week of posting. METHODS: A survey based on previous concussion research was exported to a Web-based survey website Survey Monkey. The survey comprised 2 questionnaires, one for players, and one for those involved in the game (eg, coaches and athletic trainers). The Web-based survey was administered using 2 existing Twitter accounts, with each account executing a distinct targeting strategy. A list of potential Twitter accounts to target was drawn up, together with a list of predesigned tweets. The list of accounts to target was divided into 'High-Profile' and 'Low-Profile', based on each accounts' position to attract publicity with a high social interaction potential. The potential reach (number of followers of the targeted account), and actual reach (number of retweets received by each post) between the 2 strategies were compared. The number of retweets received by each account was further analyzed to understand when the most likely time of day, and day of the week, a retweet would be received. RESULTS: The number of retweets received by a Twitter account decreased by 72% when using the 'high-profile strategy' compared with the 'low-profile strategy' (incidence rate ratio (IRR); 0.28, 95% confidence interval (CI) 0.21-0.37, P<.001). When taking into account strategy and day of the week, the IRR for the number of retweets received during the hours of 12 AM to 5:59 AM (IRR 2.98, 95% CI 1.88-4.71, P>.001) and 6 PM to 11:59 PM (IRR 1.48, 95% CI 1.05-2.09, P>.05) were significantly increased relative to 6 AM to 11:59 AM. However, posting tweets during the hours of 12 PM to 5:59 PM, decreased the IRR for retweets by 40% (IRR 0.60, 95% CI 0.46-0.79, P<.001) compared with 6 AM to 11:59 AM. Posting on a Monday (IRR 3.57, 95% CI 2.50-5.09, P<.001) or Wednesday (IRR 1.50, 95% CI 1.11-1.11, P<.01) significantly increased the IRR compared with posting on a Thursday. CONCLUSIONS: Surveys are a useful tool to measure the knowledge, attitudes, and behaviors of a given population. Strategies to improve Twitter engagement include targeting low-profile accounts, posting tweets in the morning (12 AM-11:59 AM) or late evenings (6 PM-11:59 PM), and posting on Mondays and Wednesdays.en_US
dc.language.isoenen_US
dc.publisherJMIR Publicationsen_US
dc.relation.ispartofseriesJMIR Research Protocols;
dc.titleTwitter Strategies for Web-Based Surveying: Descriptive Analysis From the International Concussion Studyen_US
dc.typeArticleen_US
dc.identifier.doihttp://doi.org/10.2196/resprot.4542
dcterms.dateAccepted2016-04-07
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionVoRen_US
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/3.0/en_US
rioxxterms.licenseref.startdate2018-02-26
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


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