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Objective This project aimed to quantify and compare Massachusetts and Georgia public school districts’ 2017–2018 winter-storm-related Twitter unplanned school closure announcements (USCA). Methods Public school district Twitter handles and National Center for Education Statistics data were obtained for Georgia and Massachusetts. Tweets were retrieved using Twitter application programming interface. Descriptive statistics and regression analyses were conducted to compare the rates of winter-storm-related USCA. Results Massachusetts had more winter storms than Georgia during the 2017–2018 winter season, but Massachusetts school districts posted winter-storm-related USCA at a 60% lower rate per affected day (adjusted rate ratio, aRR = 0.40, 95% confidence intervals, CI: 0.30, 0.52) than Georgia school districts after controlling for the student enrollments and Twitter followers count per Twitter account. A 10-fold increase in followers count was correlated with a 118% increase in USCA rate per affected day (aRR = 2.18; 95% CI: 1.74, 2.75). Georgia school districts had a higher average USCA tweet rate per winter-storm-affected day than Massachusetts school districts. A higher number of Twitter followers was associated with a higher number of USCA tweets per winter-storm-affected day. Conclusion Twitter accounts of school districts in Massachusetts had a lower tweet rate for USCA per winter-storm-affected days than those in Georgia.
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.
Background Awareness and attentiveness have implications for the acceptance and adoption of disease prevention and control measures. Social media posts provide a record of the public’s attention to an outbreak. To measure the attention of Chinese netizens to COVID-19, a pre-established nationally representative cohort of Weibo users was searched for COVID-19-related keywords in their posts. Methods COVID-19-related posts ( N =1101) were retrieved from a longitudinal cohort of 52,268 randomly sampled Weibo accounts (December 31, 2019 – February 12, 2020). Results Attention to COVID-19 was limited prior to China openly acknowledging human-to-human transmission on January 20. Following this date, attention quickly increased and has remained high over time. Particularly high levels of social media traffic appeared around when Wuhan was first placed in quarantine (January 23-24, 8-9% of the overall posts), when a scandal associated with the Red Cross Society of China occurred (February 1, 8%), and following the death of Dr. Li Wenliang (February 6-7, 11%), one of the whistleblowers reprimanded by the Chinese police in early January for discussing this outbreak online. Discussion Limited early warnings represent missed opportunities to engage citizens earlier in the outbreak. Governments should more proactively communicate early warnings to the public in a transparent manner.
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.
Background Information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. Methods Our data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. Results On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. Conclusions Broadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users. Electronic supplementary material The online version of this article (10.1186/s12889-019-6747-8) contains supplementary material, which is available to authorized users.