Which health and biomedical topics generate the most Facebook interest and the strongest citation relationships?

Why are medical research articles tweeted? The news value perspective

Counts of tweets mentioning research articles are potentially useful as social impact altmetric indicators, especially for health-related topics. One way to help understand what tweet counts indicate is to find factors that associate with the number of tweets received by articles. Using news value theory, this study examined six characteristics of research papers that may cause some articles to be more tweeted than others. For this, we manually coded 300 medical journal articles about COVID-19. A statistical analysis showed that all six factors that make articles more newsworthy according to news value theory (importance, controversy, elite nations, elite persons, scale, news prominence) associated with higher tweet counts. Since these factors are hypothesised to be general human news selection criteria, the results give new evidence that tweet counts may be indicators of general interest to members of society rather than measures of societal impact. This study also provides a new understanding of the strong positive relationship between news mentions and tweet counts for articles. Instead of news coverage attracting tweets or the other way round (journalists noticing highly tweeted articles and writing about them), the results are consistent with newsworthy characteristics of articles attracting both tweets and news mentions.

Understanding public interest and needs in health policies through the application of social network analysis on a governmental Facebook fan page

BACKGROUND

This study analyzed the interactions between agencies, policies, and the interest of the public using a social network analysis.

METHODS

Open data on the 2017 Facebook fan page of the Ministry of Health and Welfare (MoHW) in Taiwan, including 18,193 messages, were analyzed by conducting a social network analysis, NodeXL (Network Overview, Discovery and Exploration for Excel), creating visualized explorations using size volumes to present the degree of strength between agencies and policies to further calculate the network centrality indicators of agencies and policies.

RESULTS

Agencies of the "Social and Family Affairs Administration" and "Health Promotion Administration" contributed the most policy posts. The policy of "Physical and mental health promotion" entailed the most agencies to be involved. The "Department of Nursing and Health Care" received the largest public response, for which "Long-term care" received the most public interest.

CONCLUSIONS

A social network analysis of fan page of Taiwan's top level health government agency can reveal the government's most emphasized core policies, the strength of correlations between agencies and policies, and provide an understanding of public interest toward the policies.

Exploring research trends in big data across disciplines: A text mining analysis

Using big data has been a prevailing research trend in various academic fields. However, no studies have explored the scope and structure of big data across disciplines. In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between 2012 and 2017. The results revealed several topics associated with the storage, collection and analysis of large datasets; the publications were predominantly published in computational fields. Other identified research topics show the influence of big data methods and techniques in areas beyond computer science, such as education, urban informatics, business, health and medical sciences. In fact, the prevalence of these topics has increased over time. In contrast, some themes like parallel computing, network modeling and big data analytic techniques have lost their popularity in recent years. These results probably reflect the maturity of big data core topics and highlight flourishing new research trends pertinent to big data in new domains, especially in social sciences, health and medicine. Findings of this article can be beneficial for researchers and science policymakers to understand the scope and structure of big data in different academic disciplines.

Diffusing science through social networks: The case of breastfeeding communication on Twitter

Breastfeeding is one of many health practices known to support the survival and health of mother and infant, yet low breastfeeding rates persist globally. These rates may be influenced by limited diffusion of evidence-based research and guidelines from the scientific community (SC). As recently highlighted by the National Academy of Sciences, there is a need for the SC to diffuse its findings to the public more effectively online, as means to counteract the spread of misinformation. In response to this call, we gathered data from Twitter for one month from major breastfeeding hashtags resulting in an interconnected social network (n = 3,798 users). We then identified 59 influencers who disproportionately influenced information flow using social network analysis. These influencers were from the SC (e.g. academics, researchers, health care practitioners), as well as interested citizens (IC) and companies. We then conducted an ego-network analysis of influencer networks, developed ego maps, and compared diffusion metrics across the SC, IC and company influencers. We also qualitatively analyzed their tweets (n = 711) to understand the type of information being diffused. SC influencers were the least efficient communicators. Although having the highest tweeting activity (80% of tweets), they did not reach more individuals compared to IC and companies (two-step ego size: 220± 99, 188 ± 124, 169 ± 97 respectively, P = 0.28). Content analysis of tweets suggest IC are more active than the SC in diffusing evidence-based breastfeeding knowledge, with 35% of their tweets around recent research findings compared to only 12% by the SC. Nonetheless, in terms of outreach to the general public, the two-step networks of SC influences were more heterogenous than ICs (55.7 ± 5.07, 50.9 ± 12.0, respectively, P<0.001). Collectively, these findings suggest SC influencers may possess latent potential to diffuse research and evidence- based practices. However, the research suggests specific ways to enhance diffusion.

COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts

The COVID-19 pandemic requires a fast response from researchers to help address biological, medical, and public health issues to minimize its impact. In this rapidly evolving context, scholars, professionals, and the public may need to identify important new studies quickly. In response, this paper assesses the coverage of scholarly databases and impact indicators during March 21, 2020 to April 18, 2020. The rapidly increasing volume of research is particularly accessible through Dimensions, and less through Scopus, the Web of Science, and PubMed. Google Scholar’s results included many false matches. A few COVID-19 papers from the 21,395 in Dimensions were already highly cited, with substantial news and social media attention. For this topic, in contrast to previous studies, there seems to be a high degree of convergence between articles shared in the social web and citation counts, at least in the short term. In particular, articles that are extensively tweeted on the day first indexed are likely to be highly read and relatively highly cited 3 weeks later. Researchers needing wide scope literature searches (rather than health-focused PubMed or medRxiv searches) should start with Dimensions (or Google Scholar) and can use tweet and Mendeley reader counts as indicators of likely importance.