![]() In addition, we did not have demographic data, such as political affiliation, limiting more detailed analyses. 6 Although we manually reviewed tweets, restricted data to individual accounts, excluded retweets, and limited tweets per Twitter username, online manipulation (eg, “bots”) remains a potential issue. Limitations of this study include Twitter not being representative of the general population. Third, social media analysis can and should be explored to identify sources of and ways to combat misinformation. 3 Second, social media can be used to assess the public’s changing responses to public health–related communications. First, health departments, political leaders, and influencers should leverage social media to support science on health issues, especially during pandemics. This analysis has several immediate public health implications. These results suggest that some members of the public adapted beliefs in response to President Trump’s tweets and that social media may be used as a near real-time data source to capture these changing perspectives, including COVID-19–related misinformation. Analyses were performed using Excel, version 1808 (Microsoft Corp). Significance was set at a 2-sided P = .05. A χ 2 test was used to assess differences in tweet proportions for each category across periods. Unrelated tweets from each period (301, 354, and 788 tweets, respectively) were removed, leaving 5945 tweets for analysis. Tweets were restricted to 1 tweet per Twitter username by discarding any tweets after the first. Interrater reliability among 4 labelers on 93 randomly selected tweets yielded a Krippendorff α of 0.47. We randomly selected and manually labeled 3000 tweets from each period into 1 of 4 categories: (1) COVID-19 is not real, (2) COVID-19 is real but not serious, (3) COVID-19 is real and serious, and (4) unrelated or no stance ( Box). Tweets were restricted to “swing states” (Florida, Michigan, Colorado, Iowa, Minnesota, Nevada, New Hampshire, North Carolina, Ohio, Pennsylvania, Virginia, and Wisconsin), individual accounts, and non-retweets. “Not as fake as Trump claiming to have Covid #TrumpCovidHoax #TrumpNeverHadCovid19”Ībbreviation: COVID-19, coronavirus disease 2019.Ī Taken from 5945 tweets identified from September 23, 2020, to October 8, 2020. why isn’t trump paying for his medical bills?” how come real tax payers are paying thousands in medical bills for covid. “Wow, a little covid and Twitter goes e on man, it's all a hoax right? #GotWhatYouDeserved” “Stay home.even if you don’t care or believe this entire Pandemic is a hoax! It’s time to CHANGE your behavior #Covid19isarealthing” Many of those 210k people didn't even really die from the actual Chinese virus just go back to getting false info from fake news” “cdc has said most of the mask people wear are not even effective there has been no social distancing during riots no one seems to care. Time to go back to life as we knew it before this hype” We should be vigilant but if we get it we get it. In fact the survival rate is greater than 98%. Example Tweets by Belief in the Severity of COVID-19 a COVID-19 Is a Hoax ![]() The study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE) reporting guideline. This study and the consent process were exempted by the University of California, Irvine, institutional review board because all data were public. Tweets were collected from “swing states” (Florida, Michigan, Colorado, Iowa, Minnesota, Nevada, New Hampshire, North Carolina, Ohio, Pennsylvania, Virginia, and Wisconsin) ( Box), were restricted to individual accounts, excluded retweets, and were obtained from 1 of 3 periods: (1) September 23 to Octo(before President Trump’s tweet that he had contracted COVID-19) (2) October 1 to Octo(between his infection announcement and his tweet, “Don’t be afraid of COVID”) and (3) October 5 to Octo(after his “Don’t be afraid” tweet). Search criteria included COVID-19–related keywords (eg, COVID-19, coronavirus) within 15 characters of a supporting (eg, serious, real) or contradicting (eg, hoax, #CovidHoax) adjective or hashtag. Shared Decision Making and Communicationįor this cross-sectional study, from September 23 to October 8, 2020, we collected 22 800 tweets regarding public views on COVID-19 using Twitter’s free application programming interface.Scientific Discovery and the Future of Medicine.Health Care Economics, Insurance, Payment.Clinical Implications of Basic Neuroscience.Challenges in Clinical Electrocardiography.
0 Comments
Leave a Reply. |