#CDCGrandRounds and #VitalSigns: A Twitter Analysis

Jackson, A.; Mullican, Lindsay A.; Yin, Jingjing; Tse, Zion Tsz Ho; Liang, Hai; Fu, King‐Wa; Ahweyevu, Jennifer O.; Jenkins, Jimmy J.; Saroha, Nitin; Fung, Isaac Chun-Hai

doi:10.29024/aogh.2381

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…In a retrospective observational study, the effect of attaching visual cues to a tweet on its retweet frequency was found to vary across cycles of original tweets with hashtags #CDCGrandRounds and #VitalSigns. The probability ratios of retweet frequency of tweets with visual cues as compared to tweets without visual cues ranged from less than 1 to as high as 34, depending on the topic of the specific CDC Grand Round event or Vital Signs publication [ 31 ]. In a prospective, case-control crossover study of visual abstracts (graphics that summarize the main message of a scientific paper), a surgical journal found that the retweet frequency of its tweet that carries both the title of the paper and a visual abstract was 8.4 times that of a tweet with the title of the paper only (92 vs 11 retweets, P <.001) [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Contents, Followers, and Retweets of the Centers for Disease Control and Prevention’s Office of Advanced Molecular Detection (@CDC_AMD) Twitter Profile: Cross-Sectional Study

Fung¹,

J²,

La³

et al. 2018

JMIR Public Health Surveill

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BackgroundThe Office of Advanced Molecular Detection (OAMD), Centers for Disease Control and Prevention (CDC), manages a Twitter profile (@CDC_AMD). To our knowledge, no prior study has analyzed a CDC Twitter handle’s entire contents and all followers.ObjectiveThis study aimed to describe the contents and followers of the Twitter profile @CDC_AMD and to assess if attaching photos or videos to tweets posted by @CDC_AMD would increase retweet frequency.MethodsData of @CDC_AMD were retrieved on November 21, 2016. All followers (N=809) were manually categorized. All tweets (N=768) were manually coded for contents and whether photos or videos were attached. Retweet count for each tweet was recorded. Negative binomial regression models were applied to both the original and the retweet corpora.ResultsAmong the 809 followers, 26.0% (210/809) were individual health professionals, 11.6% (94/809) nongovernmental organizations, 3.3% (27/809) government agencies’ accounts, 3.3% (27/809) accounts of media organizations and journalists, and 0.9% (7/809) academic journals, with 54.9% (444/809) categorized as miscellaneous. A total of 46.9% (360/768) of @CDC_AMD’s tweets referred to the Office’s website and their current research; 17.6% (135/768) referred to their scientists’ publications. Moreover, 80% (69/86) of tweets retweeted by @CDC_AMD fell into the miscellaneous category. In addition, 43.4% (333/768) of the tweets contained photos or videos, whereas the remaining 56.6% (435/768) did not. Attaching photos or videos to original @CDC_AMD tweets increases the number of retweets by 37% (probability ratio=1.37, 95% CI 1.13-1.67, P=.002). Content topics did not explain or modify this association.ConclusionsThis study confirms CDC health communicators’ experience that original tweets created by @CDC_AMD Twitter profile sharing images or videos (or their links) received more retweets. The current policy of attaching images to tweets should be encouraged.

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Section: Discussionmentioning

confidence: 99%

Contents, Followers, and Retweets of the Centers for Disease Control and Prevention’s Office of Advanced Molecular Detection (@CDC_AMD) Twitter Profile: Cross-Sectional Study

Fung¹,

J²,

La³

et al. 2018

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

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“…The confounders chosen to be included in our regression models include (a) user characteristics (numbers of followers, friends [Twitter users whom one follows], favorites [tweets one 'likes'] and status updates [the total number of tweets one ever tweeted]) and (b) tweet-specific meta-data (the age of a tweet, hashtag count and the presence of a URL link). 15,21 In the model for the 'entire sample' we also included the day of the week, based on prior literature on the circaseptan rhythm of health information seeking as found on Google Search. 22 Thursday was chosen as the reference category because the first tweet of our sample was posted on a Thursday (13 October 2011).…”

Section: Methodsmentioning

confidence: 99%

“…12 For example, the Centers for Disease Control and Prevention (CDC) uses Twitter to host Twitter chats on Ebola and Zika 13,14 and to advertise their monthly events and publications. 15 With about 328 million monthly active users in 2017, Twitter is one of the most popular social media platforms worldwide. 16 Twitter provides a communication channel through which laypeople can advocate for science, as seen in tweets pertinent to climate change.…”

Section: Introductionmentioning

confidence: 99%

World Pneumonia Day 2011–2016: Twitter contents and retweets

Adnan

Yin

Jackson

et al. 2018

International Health

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Background: Twitter is used for World Pneumonia Day (WPD; November 12) communication. We evaluate if themes of #pneumonia tweets were associated with retweet frequency.Methods: A total of 28 181 original #pneumonia tweets were retrieved (21 November 2016), from which six subcorpora, 1 mo before and 1 mo after WPD 2011-2016, were extracted (n=6721). Underlying topics were identified via latent Dirichlet allocation and were manually coded into themes. The association of themes with retweet count was assessed via multivariable hurdle regression.Results: Compared with personal experience tweets, tweets that both raised awareness and promoted intervention were 2.62 times as likely to be retweeted (adjusted odds ratio [aOR] 2.62 [95% 1.79 to 3.85]) and if retweeted had 37% more retweets (adjusted prevalence ratio [aPR] 1.37 [95% CI 1.06 to 1.78]). Tweets that raised concerns about vaccine price were twice as likely to be retweeted (aOR 2.29 [95% CI 1.36 to 3.84]) and if retweeted, had double the retweet count (aPR 2.05 [95% CI 1.27 to 3.29]) of tweets sharing personal experience. Conclusions:The #pneumonia tweets that both raised awareness and promoted interventions and those discussing vaccine price were more likely to engage users than tweets about personal experience. These results help health professionals craft WPD messages that will engage the audience.

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“…In the past few years, our team at Georgia Southern University has met our students' educational needs through various student projects [7,14,15,[18][19][20]. Some projects focused on specific health topics, such as sentiment, contents, and retweets of vaccine-related tweets [7].…”

Section: Introductionmentioning

confidence: 99%

Pedagogical Demonstration of Twitter Data Analysis: A Case Study of World AIDS Day, 2014

Fung

Yin

Pressley

et al. 2019

Data

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As a pedagogical demonstration of Twitter data analysis, a case study of HIV/AIDS-related tweets around World AIDS Day, 2014, was presented. This study examined if Twitter users from countries with various income levels responded differently to World AIDS Day. The performance of support vector machine (SVM) models as classifiers of relevant tweets was evaluated. A manual coding of 1,826 randomly sampled HIV/AIDS-related original tweets from November 30 through December 2, 2014 was completed. Logistic regression was applied to analyze the association between the World Bank-designated income level of users’ self-reported countries and Twitter contents. To identify the optimal SVM model, 1278 (70%) of the 1826 sampled tweets were randomly selected as the training set, and 548 (30%) served as the test set. Another 180 tweets were separately sampled and coded as the held-out dataset. Compared with tweets from low-income countries, tweets from the Organization for Economic Cooperation and Development countries had 60% lower odds to mention epidemiology (adjusted odds ratio, aOR = 0.404; 95% CI: 0.166, 0.981) and three times the odds to mention compassion/support (aOR = 3.080; 95% CI: 1.179, 8.047). Tweets from lower-middle-income countries had 79% lower odds than tweets from low-income countries to mention HIV-affected sub-populations (aOR = 0.213; 95% CI: 0.068, 0.664). The optimal SVM model was able to identify relevant tweets from the held-out dataset of 180 tweets with an accuracy (F1 score) of 0.72. This study demonstrated how students can be taught to analyze Twitter data using manual coding, regression models, and SVM models.

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#CDCGrandRounds and #VitalSigns: A Twitter Analysis

Cited by 5 publications

References 16 publications

Contents, Followers, and Retweets of the Centers for Disease Control and Prevention’s Office of Advanced Molecular Detection (@CDC_AMD) Twitter Profile: Cross-Sectional Study

Contents, Followers, and Retweets of the Centers for Disease Control and Prevention’s Office of Advanced Molecular Detection (@CDC_AMD) Twitter Profile: Cross-Sectional Study

World Pneumonia Day 2011–2016: Twitter contents and retweets

Pedagogical Demonstration of Twitter Data Analysis: A Case Study of World AIDS Day, 2014

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