Lyme Disease and YouTube™: A Cross-Sectional Study of Video Contents

Objectives: Lyme 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. Methods: Most viewed English YouTubeTM videos (n = 100) were identified and manually coded for contents and sources. Results: Within 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). Conclusion: A 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

#CDCGrandRounds and #VitalSigns : A Twitter Analysis

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

#CDCGrandRounds and #VitalSigns: A Twitter Analysis

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 but 3. Of the 42 #VitalSigns cycles, 19 were statistically significant. Of these 19 cycles, six were PR > 1 and 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

Applying Survival Analysis and Count Models to Twitter Data

Twitter has a variety of information on it, health topic is one of the popular categories. We used a collection of almost 40,000 tweets extracted from Twitter with #blood pressure from January, 2014 to April, 2015 to investigate the potentially associated factors for popularity (measured by the number of retweet) as well as the survival of tweets (measured by the time frame from the first post to its last retweet). We have found the appearance of a few hashtags significantly decreased the survival of tweets. Furthermore, these hashtags increase( but some decrease) the odds of being retweeted. And other factors significantly associated with the odds include actor\u27s friends count, actor\u27s follower\u27s count, actor\u27s listed count and so on. We explored our results using R, the results do not highlight the potential of hashtag in the application of twitter

Lyme Disease and Youtube: A Cross-Sectional Study of Video Contents

Objectives: Lyme 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. Methods: Most viewed English YouTubeTM videos (n = 100) were identified and manually coded for contents and sources. Results: Within 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). Conclusion: A 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

Zika-Virus Related Photo-Sharing on Pinterest and Instagram

Communicating accurate, accessible and actionable information to diverse populations is a key component of emergency responses against the Zika virus outbreak. Public health agencies are engaging the public using fast-growing photo-sharing social media sites such as Pinterest and Instagram. In this cross-sectional study, 616 Pinterest photos (keyword: “zika” AND “virus”; the maximum number of photos that we were able to retrieve via web scraping) and 616 Instagram photos (#zikavirus; randomly selected from 9370 Instagram photos retrieved via Instagram Application Programming Interface) were retrieved on April 3, 2016. Two trained individuals manually coded photos based on their relevance to Zika virus, words embedded, language and their content categories (any category that applies). Among our samples, 47% (290/616) of Pinterest photos and 23% (144/616) of Instagram photos were relevant to Zika virus. Words were embedded in 57% (164/290) of relevant Pinterest photos and 100% (144/144) of relevant Instagram photos. Among the photos with embedded words, more Instagram photos were in Spanish and Portuguese (77/144, 53%) than Pinterest (14/164, 9%) (

Zika-Virus-Related Photo Sharing on Pinterest and Instagram

Pinterest (San Francisco, CA) and Instagram (Menlo Park, CA) are 2 popular photo-sharing social media platforms among young individuals. We assessed differences between Instagram and Pinterest in relaying photographic information regarding Zika virus. Specifically, we investigated whether the percentage of Zika-virus-related photos with Spanish or Portuguese texts embedded therein was higher for Instagram than for Pinterest and whether the contents of Zika-virus-related photos shared on Pinterest were different from those shared on Instagram. We retrieved and manually coded 616 Pinterest (key words: “zika” AND “virus”) and 616 Instagram (hashtag: #zikavirus) photos. Among the manually coded samples, 47% (290/616) of Pinterest photos and 23% (144/616) of Instagram photos were relevant to Zika virus. Words were embedded in 57% (164/290) of relevant Pinterest photos and all 144 relevant Instagram photos. Among the photos with embedded words, photos in Spanish or Portuguese were more prevalent on Instagram (77/144, 53%) than on Pinterest (14/164, 9%). There were more Zika-virus-related photos on Instagram than on Pinterest pertinent to Zika virus prevention (59/144, 41%, versus 41/290, 14%; P Pinterest and Instagram are similar platforms for Zika virus prevention communication