Users' Beliefs Toward Physical Distancing in Facebook Pages of Public Health Authorities During COVID-19 Pandemic in Early 2020.

This study aims to describe Facebook users' beliefs toward physical distancing measures implemented during the Coronavirus disease (COVID-19) pandemic using the key constructs of the health belief model. A combination of rule-based filtering and manual classification methods was used to classify user comments on COVID-19 Facebook posts of three public health authorities: Centers for Disease Control and Prevention of the United States, Public Health England, and Ministry of Health, Singapore. A total of 104,304 comments were analyzed for posts published between 1 January, 2020, and 31 March, 2020, along with COVID-19 cases and deaths count data from the three countries. Findings indicate that the perceived benefits of physical distancing measures (n = 3,463; 3.3%) was three times higher than perceived barriers (n = 1,062; 1.0%). Perceived susceptibility to COVID-19 (n = 2,934; 2.8%) was higher compared with perceived severity (n = 2,081; 2.0%). Although susceptibility aspects of physical distancing were discussed more often at the start of the year, mentions on the benefits of intervention emerged stronger toward the end of the analysis period, highlighting the shift in beliefs. The health belief model is useful for understanding Facebook users' beliefs at a basic level, and it provides a scope for further improvement.

Digital Support for Renal Patients Before and During the COVID-19 Pandemic: Examining the Efforts of Singapore Social Service Agencies in Facebook.

During the coronavirus disease 2019 (COVID-19) pandemic, social service agencies (SSAs) play a crucial role in supporting renal patients, who are particularly vulnerable to infections. Social media platforms such as Facebook, serves as an effective medium for these SSAs to disseminate information. Content analysis of the SSAs' Facebook posts can provide insights on whether Facebook has been adequately utilized during the COVID-19 pandemic and enable SSAs to improve their social media use in future pandemics. This study aimed to compare renal-related SSAs' Facebook post content before and during the COVID-19 pandemic. Facebook posts of three SSAs National Kidney Foundation (NKF), Kidney Dialysis Foundation (KDF), and Muslim Kidney Action Association (MKAC), posted during the pre-COVID-19 period (January 23, 2019 to June 2, 2019) and the peri-COVID-19 period (January 23, 2020 to June 1, 2020) were extracted. A classification scheme was developed by two coders with themes derived inductively and deductively. Each Facebook post was assigned with a theme. Quantitative analyses indicate that the number of Facebook posts increased from 115 in the pre-COVID-19 period to 293 in the peri-COVID-19 period. During peri-COVID-19, posts regarding lifestyle changes, donations and infectious disease surfaced. While the proportion of posts about encouraging kindness increased from one to 77 posts, the proportion of posts about community-based events and psychosocial support decreased from 44 to 15 posts and 17 to 10 posts respectively during the two periods. Facebook was found to be well-utilized by two of the three renal SSAs in engaging their beneficiaries during the pandemic. During future pandemics, renal SSAs should place emphasis on posts related to psychosocial support and encouraging kindness. Further studies are required to ascertain the impact of COVID-19 from the perspective of renal patients and also to validate the classification scheme which was developed in this study. The study's methodology and classification scheme can be used to guide future studies for evaluating the social media outreach performance of renal health support groups.

Impact of COVID-19 on the outreach strategy of cancer social service agencies in Singapore: A pre-post analysis with Facebook data.

The Singapore government implemented multiple restrictive measures as the novel coronavirus infection (COVID-19) spread through the community, thereby affecting the support service of cancer-related social service agencies (cancer-SSAs). We are interested to understand how Singapore's cancer-SSAs utilized the social media platform Facebook to overcome the restrictions which were introduced due to COVID-19. Facebook posts from two prominent cancer-SSAs 365 Cancer Prevention Society (365CPS) and Singapore Cancer Society (SCS) between comparable periods in 2019 and 2020 were extracted. These posts were categorized using a classification scheme which was specifically developed by two coders with adequate intercoder reliability. Statistical analyses were performed to determine if there was a significant difference in the frequency of posts between 2019 and 2020, and across three specific periods in 2020. Results indicate that 365CPS appears to have adapted to the pandemic by increasing their posting frequency on Facebook in 2020, but the same was not evident for SCS. However, both SSAs tweaked their social media outreach strategy in line with social distancing measures, publishing posts detailing activities that beneficiaries can participate from home such as healthy recipes and virtual events. SSAs can scale up their efforts to achieve a higher level of health promotion and support for their beneficiaries. Further qualitative studies are required to ascertain the impact of COVID-19 from the perspective of beneficiaries and validate the classification scheme which was developed in this study. The classification scheme could be used in ascertaining the quality of social media communication between public/private support groups and patients.

Use of Health Belief Model-Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study.

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.

Measuring the Outreach Efforts of Public Health Authorities and the Public Response on Facebook During the COVID-19 Pandemic in Early 2020: Cross-Country Comparison.

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.