Background The coronavirus disease (COVID-19) pandemic presents one of the most challenging global crises at the dawn of a new decade. Public health authorities (PHAs) are increasingly adopting the use of social media such as Facebook to rapidly communicate and disseminate pandemic response measures to the public. Understanding of communication strategies across different PHAs and examining the public response on the social media landscapes can help improve practices for disseminating information to the public. Objective This study aims to examine COVID-19-related outreach efforts of PHAs in Singapore, the United States, and England, and the corresponding public response to these outreach efforts on Facebook. Methods Posts and comments from the Facebook pages of the Ministry of Health (MOH) in Singapore, the Centers for Disease Control and Prevention (CDC) in the United States, and Public Health England (PHE) in England were extracted from January 1, 2019, to March 18, 2020. Posts published before January 1, 2020, were categorized as pre-COVID-19, while the remaining posts were categorized as peri-COVID-19 posts. COVID-19-related posts were identified and classified into themes. Metrics used for measuring outreach and engagement were frequency, mean posts per day (PPD), mean reactions per post, mean shares per post, and mean comments per post. Responses to the COVID-19 posts were measured using frequency, mean sentiment polarity, positive to negative sentiments ratio (PNSR), and positive to negative emotions ratio (PNER). Toxicity in comments were identified and analyzed using frequency, mean likes per toxic comment, and mean replies per toxic comment. Trend analysis was performed to examine how the metrics varied with key events such as when COVID-19 was declared a pandemic. Results The MOH published more COVID-19 posts (n=271; mean PPD 5.0) compared to the CDC (n=94; mean PPD 2.2) and PHE (n=45; mean PPD 1.4). The mean number of comments per COVID-19 post was highest for the CDC (mean CPP 255.3) compared to the MOH (mean CPP 15.6) and PHE (mean CPP 12.5). Six major themes were identified, with posts about prevention and safety measures and situation updates being prevalent across the three PHAs. The themes of the MOH’s posts were diverse, while the CDC and PHE posts focused on a few themes. Overall, response sentiments for the MOH posts (PNSR 0.94) were more favorable compared to response sentiments for the CDC (PNSR 0.57) and PHE (PNSR 0.55) posts. Toxic comments were rare (0.01%) across all PHAs. Conclusions PHAs’ extent of Facebook use for outreach purposes during the COVID-19 pandemic varied among the three PHAs, highlighting the strategies and approaches that other PHAs can potentially adopt. Our study showed that social media analysis was capable of providing insights about the communication strategies of PHAs during disease outbreaks.

Background Public health authorities have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions toward such interventions should be identified to enable public health authorities to effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, with the aim of understanding the health behaviors of the public. Objective This study is aimed at developing and evaluating deep learning–based text classification models for classifying social media content posted during the COVID-19 outbreak, using the four key constructs of the HBM. We will specifically focus on content related to the physical distancing interventions put forth by public health authorities. We intend to test the model with a real-world case study. Methods The data set for this study was prepared by analyzing Facebook comments that were posted by the public in response to the COVID-19–related posts of three public health authorities: the Ministry of Health of Singapore (MOH), the Centers for Disease Control and Prevention, and Public Health England. The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated data set of 16,752 comments, gated recurrent unit–based recurrent neural network models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used to evaluate the model. Specificity, sensitivity, and balanced accuracy were used to evaluate the classification results in the MOH case study. Results The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91, and 0.94 for the constructs of perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the case study with MOH Facebook comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits, respectively. In addition, sensitivity was 79.6% and 81.5% for perceived susceptibility and perceived barriers, respectively. The classification models were able to accurately predict trends in the prevalence of the constructs for the time period examined in the case study. Conclusions The deep learning–based text classifiers developed in this study help to determine public perceptions toward physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize the health behaviors of the public through the lens of social media. In future studies, we intend to extend the model to study public perceptions of other important interventions by public health authorities.

Recently, social media has become a potentially new way for scholarly journals to disseminate and evaluate research outputs. Scholarly journals have started promoting their research articles to a wide range of audiences via social media platforms. This article aims to investigate the social media presence of scholarly journals across disciplines. We extracted journals from Web of Science and searched for the social media presence of these journals on Facebook and Twitter. Relevant metrics and content relating to the journals' social media accounts were also crawled for data analysis. From our results, the social media presence of scholarly journals lies between 7.1% and 14.2% across disciplines; and it has shown a steady increase in the last decade. The popularity of scholarly journals on social media is distinct across disciplines. Further, we investigated whether social media metrics of journals can predict the Journal Impact Factor (JIF). We found that the number of followers and disciplines have significant effects on the JIF. In addition, a word co‐occurrence network analysis was also conducted to identify popular topics discussed by scholarly journals on social media platforms. Finally, we highlight challenges and issues faced in this study and discuss future research directions.

This research was supported by the National Research Foundation Singapore under its National Innovation Challenge on Active and Confident Ageing (Award No. MOH/NIC/CAHIG03/2016) and administered by the Singapore Ministry of Health's National Medical Research Council. This research was also supported by the National Research Foundation within the Prime Minister's Office of Singapore, under its Science of Research, Innovation and Enterprise Programme (SRIE Award No. NRF2014-NRF-SRIE001-019). The authors have no relevant conflicts of interest to disclose.

Scholarly communication has the scope to transcend the limitations of the physical world through social media's extended coverage and shortened information paths. Accordingly, publishers have created profiles for their journals in Twitter to promote their publications and to initiate discussions with public. This paper investigates the Twitter presence of humanities and social sciences (HSS) journal titles obtained from mainstream citation indices, by analysing the interaction and communication patterns. This study utilizes webometric data collection, descriptive analysis, and social network analysis. Findings indicate that the presence of HSS journals in Twitter across disciplines is not yet substantial. Sharing of general websites appears to be the key activity performed by HSS journals in Twitter. Among them, web content from news portals and magazines are highly disseminated. Sharing of research articles and retweeting was not majorly observed. Inter‐journal communication is apparent within the same citation index, but it is very minimal with journals from the other index. However, there seems to be an effort to broaden communication beyond the research community, reaching out to connect with the public.