Pinterest and Instagram are similar platforms for Zika virus prevention communication. (Disaster Med Public Health Preparedness. 2017;11:656-659).
ObjectivesLyme disease is the most common tick-borne disease. People seek health information on Lyme disease from YouTubeTM videos. In this study, we investigated if the contents of Lyme disease-related YouTubeTM videos varied by their sources.MethodsMost viewed English YouTubeTM videos (n = 100) were identified and manually coded for contents and sources.ResultsWithin the sample, 40 videos were consumer-generated, 31 were internet-based news, 16 were professional, and 13 were TV news. Compared with consumer-generated videos, TV news videos were more likely to mention celebrities (odds ratio [OR], 10.57; 95% confidence interval [CI], 2.13–52.58), prevention of Lyme disease through wearing protective clothing (OR, 5.63; 95% CI, 1.23–25.76), and spraying insecticides (OR, 7.71; 95% CI, 1.52–39.05).ConclusionA majority of the most popular Lyme disease-related YouTubeTM videos were not created by public health professionals. Responsible reporting and creative video-making facilitate Lyme disease education. Partnership with YouTubeTM celebrities to co-develop educational videos may be a future direction.
Background:The CDC hosts monthly panel presentations titled ‘Public Health Grand Rounds’ and publishes monthly reports known as Vital Signs. Hashtags #CDCGrandRounds and #VitalSigns were used to promote them on Twitter.Objectives:This study quantified the effect of hashtag count, mention count, and URL count and attaching visual cues to #CDCGrandRounds or #VitalSigns tweets on their retweet frequency.Methods:Through Twitter Search Application Programming Interface, original tweets containing the hashtag #CDCGrandRounds (n = 6,966; April 21, 2011–October 25, 2016) and the hashtag #VitalSigns (n = 15,015; March 19, 2013–October 31, 2016) were retrieved respectively. Negative binomial regression models were applied to each corpus to estimate the associations between retweet frequency and three predictors (hashtag count, mention count, and URL link count). Each corpus was sub-set into cycles (#CDCGrandRounds: n = 58, #VitalSigns: n = 42). We manually coded the 30 tweets with the highest number of retweets for each cycle, whether it contained visual cues (images or videos). Univariable negative binomial regression models were applied to compute the prevalence ratio (PR) of retweet frequency for each cycle, between tweets with and without visual cues.Findings:URL links increased retweet frequency in both corpora; effects of hashtag count and mention count differed between the two corpora. Of the 58 #CDCGrandRounds cycles, 29 were found to have statistically significantly different retweet frequencies between tweets with and without visual cues. Of these 29 cycles, one had a PR estimate < 1; twenty-four, PR > 1 but < 3; and four, PR > 3. Of the 42 #VitalSigns cycles, 19 were statistically significant. Of these 19 cycles, six were PR > 1 and < 3; and thirteen, PR > 3.Conclusions:The increase of retweet frequency through attaching visual cues varied across cycles for original tweets with #CDCGrandRounds and #VitalSigns. Future research is needed to determine the optimal choice of visual cues to maximize the influence of public health tweets.
BACKGROUND For pandemic preparedness, researchers used online systematic searches to track unplanned school closures (USCs). We determine if Twitter provides complementary data. METHODS Twitter handles of Michigan public schools and school districts were identified. All tweets associated with these handles were downloaded. USC‐related tweets were identified using 5 keywords. Descriptive statistics and multivariable logistic regression were performed in R. RESULTS Among 3469 Michigan public schools, 2003 maintained their own active Twitter accounts or belonged to school districts with active Twitter accounts. Of these 2003 schools, in 2015‐2016 school year, at least 1 USC announcement was identified for 349 schools via the current method only, 678 schools via Twitter only, and 562 schools via both methods. No USC announcements were identified for 414 schools. Rural schools were less likely than city schools to have active Twitter coverage (adjusted relative risk [adjRR] = 0.3956, 95% confidence interval [CI] 0.3312‐0.4671), and to announce USCs on Twitter (adjRR = 0.5692, 95% CI 0.4645‐0.6823), but more likely to have USCs identified by the current method (adjRR = 1.4545, 95% CI 1.3545‐1.5490). CONCLUSIONS Each method identified USCs that were missed by the other. Our results suggested that identifying USCs on Twitter is complementary to the current method.
This study examines the one-way information diffusion and two-way dialogic engagement present in public health Twitter chats. Network analysis assessed whether Twitter chats adhere to one of the key principles for online dialogic communication, the dialogic loop (Kent & Taylor, 1998) for four public health-related chats hosted by CDC Twitter accounts. The features of the most retweeted accounts and the most retweeted tweets also were examined. The results indicate that very little dialogic engagement took place. Moreover, the chats seemed to function as pseudoevents primarily used by organizations as opportunities for creating content. However, events such as #PublicHealthChat may serve as important opportunities for gaining attention for issues on social media. Implications for using social media in public interest communications are discussed.
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