No abstract

Engaging social media key opinion leaders to facilitate health education about vaccination in their tweets may allow reaching a wider audience online.

Twitter is a social media platform where over 500 million people worldwide publish their ideas and discuss diverse topics, including their health conditions and public health events. Twitter has proved to be an important source of health-related information on the Internet, given the amount of information that is shared by both citizens and official sources. Twitter provides researchers with a real-time source of public health information on a global scale, and can be very important in public health research. Classifying Twitter data into topics or categories is helpful to better understand how users react and communicate. A literature review is presented on the use of mining Twitter data or similar short-text datasets for public health applications. Each method is analyzed for ways to use Twitter data in public health surveillance. Papers in which Twitter content was classified according to users or tweets for better surveillance of public health were selected for review. Only papers published between 2010–2017 were considered. The reviewed publications are distinguished by the methods that were used to categorize the Twitter content in different ways. While comparing studies is difficult due to the number of different methods that have been used for applying Twitter and interpreting data, this state-of-the-art review demonstrates the vast potential of utilizing Twitter for public health surveillance purposes.

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.

A retrospective cohort study, using the electronic medical records of Kaiser Permanente Northern California (2011)(2012)(2013)(2014)(2015), included 560 robotic and 6785 conventional laparoscopic cases with 1836 "complex" patients (25%). The average operative time was 152 minutes (robotic) vs 157 minutes (conventional) laparoscopic hysterectomy. Complex surgical cases averaged 190 minutes and noncomplex cases averaged 144 minutes. For women with complex disease, the robotic approach, when used by a higher-volume surgeon, may be associated with shorter operative time and slightly less blood loss, but not with lower risk of complications.

Privacy is a culturally specific phenomenon. As social media platforms are going global, questions concerning privacy practices in a cross-cultural context become increasingly important. The purpose of this study is to examine cultural variations of privacy settings and self-disclosure of geolocation on Twitter. We randomly selected 3.3 million Twitter accounts from more than 100 societies. Results revealed considerable cultural and societal differences. Privacy setting in collectivistic societies was more effective in encouraging self-disclosure; whereas it appeared to be less important for users in individualistic societies. Internet penetration was also a significant factor in predicting both the adoption of privacy setting and geolocation self-disclosure. However, we did not find any direct relationships between cultural values and self-disclosure.

Objective: 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 coronavirus disease 2019 (COVID-19), a pre-established nationally representative cohort of Weibo users was searched for COVID-19-related key words 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 who was reprimanded by the Chinese police in early January for discussing this outbreak online. Conclusion: 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.

Replication is an essential requirement for scientific discovery. The current study aims to generalize and replicate 10 propositions made in previous Twitter studies using a representative dataset. Our findings suggest 6 out of 10 propositions could not be replicated due to the variations of data collection, analytic strategies employed, and inconsistent measurements. The study’s contributions are twofold: First, it systematically summarized and assessed some important claims in the field, which can inform future studies. Second, it proposed a feasible approach to generating a random sample of Twitter users and its associated ego networks, which might serve as a solution for answering social-scientific questions at the individual level without accessing the complete data archive.