Bhattacharya Sanmitra, Srinivasan Padmini, Polgreen Phil
Department of Computer Science, The University of Iowa, Iowa City, Iowa, United States of America.
Department of Internal Medicine, The University of Iowa, Iowa City, Iowa, United States of America.
PLoS One. 2014 Nov 7;9(11):e112235. doi: 10.1371/journal.pone.0112235. eCollection 2014.
To investigate factors associated with engagement of U.S. Federal Health Agencies via Twitter. Our specific goals are to study factors related to a) numbers of retweets, b) time between the agency tweet and first retweet and c) time between the agency tweet and last retweet.
We collect 164,104 tweets from 25 Federal Health Agencies and their 130 accounts. We use negative binomial hurdle regression models and Cox proportional hazards models to explore the influence of 26 factors on agency engagement. Account features include network centrality, tweet count, numbers of friends, followers, and favorites. Tweet features include age, the use of hashtags, user-mentions, URLs, sentiment measured using Sentistrength, and tweet content represented by fifteen semantic groups.
A third of the tweets (53,556) had zero retweets. Less than 1% (613) had more than 100 retweets (mean = 284). The hurdle analysis shows that hashtags, URLs and user-mentions are positively associated with retweets; sentiment has no association with retweets; and tweet count has a negative association with retweets. Almost all semantic groups, except for geographic areas, occupations and organizations, are positively associated with retweeting. The survival analyses indicate that engagement is positively associated with tweet age and the follower count.
Some of the factors associated with higher levels of Twitter engagement cannot be changed by the agencies, but others can be modified (e.g., use of hashtags, URLs). Our findings provide the background for future controlled experiments to increase public health engagement via Twitter.
调查美国联邦卫生机构通过推特进行互动的相关因素。我们的具体目标是研究与以下方面相关的因素：a）转发数；b）机构推文与首次转发之间的时间间隔；c）机构推文与最后一次转发之间的时间间隔。
我们收集了来自25个联邦卫生机构及其130个账户的164,104条推文。我们使用负二项式障碍回归模型和Cox比例风险模型来探究26个因素对机构互动的影响。账户特征包括网络中心性、推文数量、好友数、关注者数和收藏数。推文特征包括发布时长、主题标签的使用、用户提及、网址、使用Sentistrength测量的情感倾向，以及由十五个语义组表示的推文内容。
三分之一的推文（53,556条）没有转发。不到1%（613条）的推文转发数超过100次（平均转发数 = 284次）。障碍分析表明，主题标签、网址和用户提及与转发呈正相关；情感倾向与转发无关；推文数量与转发呈负相关。除地理区域、职业和组织外，几乎所有语义组都与转发呈正相关。生存分析表明，互动与推文发布时长和关注者数量呈正相关。
一些与推特较高互动水平相关的因素机构无法改变，但其他因素可以修改（例如，主题标签、网址的使用）。我们的研究结果为未来通过推特提高公众健康参与度的对照实验提供了背景。
