scholarly journals Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020 (Preprint)

2020 ◽  
Author(s):  
Daisy Massey ◽  
Chenxi Huang ◽  
Yuan Lu ◽  
Alina Cohen ◽  
Yahel Oren ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Language Processing ◽  
Daily Life ◽  
The United States ◽  
Media Analysis ◽  
Prevention Measures ◽  
Cross Sectional ◽  
Health Measures ◽  
The Us

BACKGROUND The coronavirus disease 2019 (COVID-19) has continued to spread in the US and globally. Closely monitoring public engagement and perception of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. OBJECTIVE To measure the public’s behaviors and perceptions regarding COVID-19 and its daily life effects during the recent 5 months of the pandemic. METHODS Natural language processing (NLP) algorithms were used to identify COVID-19 related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged, and sensitivity and specificity were both calculated to validate the classification of posts. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the US. RESULTS The final sample size included 9,065,733 posts, 70% of which were sourced from the US. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the US beginning in October. Additionally, counter to reports from March and April, discussion was more focused on daily life topics (69%), compared with COVID-19 in general (37%) and COVID-19 public health measures (20%). CONCLUSIONS There was a decline in COVID-19-related social media discussion sourced mainly from the US, even as COVID-19 cases in the US have increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures until a vaccine is widely available to the public.

scholarly journals Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020

2021 ◽  
Author(s):  
Daisy Massey ◽  
Chenxi Huang ◽  
Yuan Lu ◽  
Alina Cohen ◽  
Yahel Oren ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Language Processing ◽  
Daily Life ◽  
The United States ◽  
Media Analysis ◽  
Prevention Measures ◽  
Cross Sectional ◽  
Health Measures ◽  
The Us

AbstractBackgroundThe coronavirus disease 2019 (COVID-19) has continued to spread in the US and globally. Closely monitoring public engagement and perception of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs.ObjectiveTo measure the public’s behaviors and perceptions regarding COVID-19 and its daily life effects during the recent 5 months of the pandemic.MethodsNatural language processing (NLP) algorithms were used to identify COVID-19 related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged, and sensitivity and specificity were both calculated to validate the classification of posts. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the US.ResultsThe final sample size included 9,065,733 posts, 70% of which were sourced from the US. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the US beginning in October. Additionally, counter to reports from March and April, discussion was more focused on daily life topics (69%), compared with COVID-19 in general (37%) and COVID-19 public health measures (20%).ConclusionsThere was a decline in COVID-19-related social media discussion sourced mainly from the US, even as COVID-19 cases in the US have increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures until a vaccine is widely available to the public.

scholarly journals Exploring the Role of Media Sources on COVID-19–Related Discrimination Experiences and Concerns Among Asian People in the United States: Cross-Sectional Survey Study (Preprint)

2020 ◽  
Author(s):  
Nan Yu ◽  
Shuya Pan ◽  
Chia-chen Yang ◽  
Jiun-Yi Tsai
Keyword(s):  
Public Health ◽  
United States ◽  
Social Media ◽  
Asian Americans ◽  
Asian American ◽  
The United States ◽  
Cross Sectional ◽  
Racial Biases ◽  
Asian People

BACKGROUND Media coverage and scholarly research have reported that Asian people who reside in the United States have been the targets of racially motivated incidents during the COVID-19 pandemic. OBJECTIVE This study aimed to examine the types of discrimination and worries experienced by Asians and Asian Americans living in the United States during the pandemic, as well as factors that were associated with everyday discrimination experience and concerns about future discrimination that the Asian community may face. METHODS A cross-sectional online survey was conducted. A total of 235 people who identified themselves as Asian or Asian American and resided in the United States completed the questionnaire. RESULTS Our study suggested that up to a third of Asians surveyed had experienced some type of discrimination. Pooling the responses “very often,” “often,” and “sometimes,” the percentages for each experienced discrimination type ranged between 14%-34%. In total, 49%-58% of respondents expressed concerns about discrimination in the future. The most frequently experienced discrimination types, as indicated by responses “very often” and “often,” were “people act as if they think you are dangerous” (25/235, 11%) and “being treated with less courtesy or respect” (24/235, 10%). About 14% (32/235) of individuals reported very often, often, or sometimes being threatened or harassed. In addition, social media use was significantly associated with a higher likelihood of experiencing discrimination (β=.18, <i>P</i>=.01) and having concerns about future episodes of discrimination the community may face (β=.20, <i>P</i>=.005). Use of print media was also positively associated with experiencing discrimination (β=.31, <i>P</i>&lt;.001). CONCLUSIONS Our study provided important empirical evidence regarding the various types of discrimination Asians residing in the United States experienced or worried about during the COVID-19 pandemic. The relationship between media sources and the perception of racial biases in this group was also identified. We noted the role of social media in reinforcing the perception of discrimination experience and concerns about future discrimination among Asians during this outbreak. Our results indicate several practical implications for public health agencies. To reduce discrimination against Asians during the pandemic, official sources and public health professionals should be cognizant of the possible impacts of stigmatizing cues in media reports on activating racial biases. Furthermore, Asians or Asian Americans could also be informed that using social media to obtain COVID-19 information is associated with an increase in concerns about future discrimination, and thus they may consider approaching this media source with caution.

scholarly journals Artificial intelligence-enabled analysis of UK and US public attitudes on Twitter and Facebook towards COVID-19 vaccination (Preprint)

2020 ◽  
Author(s):  
Amir Hussain ◽  
Ahsen Tahir ◽  
Zain Hussain ◽  
Zakariya Sheikh ◽  
Kia Dashtipour ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
Vaccine Development ◽  
Public Attitudes ◽  
Public Attitude ◽  
Media Analysis ◽  
Public Confidence ◽  
The Us ◽  
The Uk

UNSTRUCTURED Background: Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern. Methods: Over 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural-language processing and deep learning-based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual- eading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis. Results: We found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly. Conclusions: AI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.

scholarly journals Exploring the Role of Media Sources on COVID-19–Related Discrimination Experiences and Concerns Among Asian People in the United States: Cross-Sectional Survey Study

10.2196/21684 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e21684
Author(s):  
Nan Yu ◽  
Shuya Pan ◽  
Chia-chen Yang ◽  
Jiun-Yi Tsai
Keyword(s):  
Public Health ◽  
United States ◽  
Social Media ◽  
Asian Americans ◽  
Asian American ◽  
The United States ◽  
Cross Sectional ◽  
Racial Biases ◽  
Asian People

Background Media coverage and scholarly research have reported that Asian people who reside in the United States have been the targets of racially motivated incidents during the COVID-19 pandemic. Objective This study aimed to examine the types of discrimination and worries experienced by Asians and Asian Americans living in the United States during the pandemic, as well as factors that were associated with everyday discrimination experience and concerns about future discrimination that the Asian community may face. Methods A cross-sectional online survey was conducted. A total of 235 people who identified themselves as Asian or Asian American and resided in the United States completed the questionnaire. Results Our study suggested that up to a third of Asians surveyed had experienced some type of discrimination. Pooling the responses “very often,” “often,” and “sometimes,” the percentages for each experienced discrimination type ranged between 14%-34%. In total, 49%-58% of respondents expressed concerns about discrimination in the future. The most frequently experienced discrimination types, as indicated by responses “very often” and “often,” were “people act as if they think you are dangerous” (25/235, 11%) and “being treated with less courtesy or respect” (24/235, 10%). About 14% (32/235) of individuals reported very often, often, or sometimes being threatened or harassed. In addition, social media use was significantly associated with a higher likelihood of experiencing discrimination (β=.18, P=.01) and having concerns about future episodes of discrimination the community may face (β=.20, P=.005). Use of print media was also positively associated with experiencing discrimination (β=.31, P<.001). Conclusions Our study provided important empirical evidence regarding the various types of discrimination Asians residing in the United States experienced or worried about during the COVID-19 pandemic. The relationship between media sources and the perception of racial biases in this group was also identified. We noted the role of social media in reinforcing the perception of discrimination experience and concerns about future discrimination among Asians during this outbreak. Our results indicate several practical implications for public health agencies. To reduce discrimination against Asians during the pandemic, official sources and public health professionals should be cognizant of the possible impacts of stigmatizing cues in media reports on activating racial biases. Furthermore, Asians or Asian Americans could also be informed that using social media to obtain COVID-19 information is associated with an increase in concerns about future discrimination, and thus they may consider approaching this media source with caution.

scholarly journals The Design and Methodology of the Ohio COVID-19 Survey

2021 ◽  
Vol 4 (1) ◽  
pp. 5-10
Author(s):  
Marcus E. Berzofsky ◽  
Naomi Freedner ◽  
Caroline Scruggs ◽  
Robert Ashmead ◽  
Timothy Sahr ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care Systems ◽  
The United States ◽  
Mitigation Strategies ◽  
Health Issues ◽  
Cross Sectional ◽  
Policy Makers ◽  
Health Measures ◽  
High Participation ◽  
Care Systems

Background: Governments worldwide are balancing contrasting needs to curtail the toll that coronavirus disease 2019 (COVID-19) takes on lives and health care systems and to preserve their economies. To support decisions, data that simultaneously measure the health status of the population and the economic impact of COVID-19 mitigation strategies are needed. In the United States, prior to the onset of COVID-19, surveys or tracking systems usually focused on public health or economic indicators, but not both. However, tracking public health and economic measures together allow policy makers and epidemiologists to understand how policy and program decisions are associated. The Ohio COVID-19 Survey (OCS) attempts to track both measures in Ohio as one of the first statewide population surveys on COVID-19. To achieve this there are several methodological challenges which need to be overcome. Methods: The OCS utilizes a representative panel offering both cross-sectional and longitudinal analyses. It targets 700 to 1000 respondents per week for a total of 12 600 to 18 000 respondents over an 18-week period. Leveraging a sample of 24 000 adult Ohioans developed from a statewide population health survey conducted in fall 2019, the OCS produces weekly economic and health measures that can be compared to baseline measures obtained before the COVID-19 pandemic began. Results: The OCS was able to quickly launch and achieve high participation (45.2%) and retention across waves. Conclusion: The OCS demonstrates how it is possible to leverage an existing health-based survey in Ohio to generate a panel which can be used to quickly track fast-breaking health issues like COVID-19.

scholarly journals 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 (Preprint)

2020 ◽  
Author(s):  
Aravind Sesagiri Raamkumar ◽  
Soon Guan Tan ◽  
Hwee Lin Wee
Keyword(s):  
Public Health ◽  
United States ◽  
Social Media ◽  
Communication Strategies ◽  
The United States ◽  
Media Analysis ◽  
Public Response ◽  
The Public ◽  
Health Authorities ◽  
Public Health Authorities

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.

scholarly journals Viewing the US presidential electoral map through the lens of public health

2020 ◽  
Author(s):  
Tymor Carpenter Hamamsy ◽  
Michael Danziger ◽  
Jonathan Nagler ◽  
Richard Bonneau
Keyword(s):  
Public Health ◽  
The United States ◽  
County Level ◽  
Health Trends ◽  
Health And Wellbeing ◽  
Health Measures ◽  
Significant Relationships ◽  
The Us ◽  
2016 Election ◽  
The Relationship

Health, disease, and mortality vary greatly at the county level, and there are strong geographical trends of disease. Healthcare is a top priority for voters in the United States, and it is important to examine the relationship between voting patterns at the county level and health, disease, and mortality. We perform a comprehensive analysis of the relationship between voting patterns and over 150 different public health and wellbeing variables, comparing counties in all states, including counties in 2016 battleground states, and counties in states that flipped from Democrat to Republican from 2012 to 2016. We also investigate county-level health trends over the last 30+ years and find statistically significant relationships between a number of health measures and the voting patterns of counties in presidential elections. Collectively, this data exhibits a strong pattern: counties that voted Republican in the 2016 election counties are "sicker" than those that voted Democrat.

scholarly journals Monitoring the opioid epidemic via social media discussions

2021 ◽  
Author(s):  
Adam Lavertu ◽  
Tymor Hamamsy ◽  
Russ B Altman
Keyword(s):  
Public Health ◽  
Social Media ◽  
Language Processing ◽  
Public Health Surveillance ◽  
The United States ◽  
Health Surveillance ◽  
Opioid Epidemic ◽  
Strongly Correlated ◽  
Drug Classes ◽  
Social Media Platforms

AbstractThe opioid epidemic persists in the United States; in 2019, annual drug overdose deaths increased by 4.6% to 70,980, including 50,042 opioid-related deaths. The widespread abuse of opioids across geographies and demographics and the rapidly changing dynamics of abuse require reliable and timely information to monitor and address the crisis. Social media platforms include petabytes of participant-generated data, some of which, offers a window into the relationship between individuals and their use of drugs. We assessed the utility of Reddit data for public health surveillance, with a focus on the opioid epidemic. We built a natural language processing pipeline to identify opioid-related comments and created a cohort of 1,689,039 geo-located Reddit users, each assigned to a city and state. We followed these users over a period of 10+ years and measured their opioid-related activity over time. We benchmarked the activity of this cohort against CDC overdose death rates for different drug classes and NFLIS drug report rates. Our Reddit-derived rates of opioid discussion strongly correlated with external benchmarks on the national, regional, and city level. During the period of our study, kratom emerged as an active discussion topic; we analyzed mentions of kratom to understand the dynamics of its use. We also examined changes in opioid discussions during the COVID-19 pandemic; in 2020, many opioid classes showed marked increases in discussion patterns. Our work suggests the complementary utility of social media as a part of public health surveillance activities.

scholarly journals Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations

2020 ◽  
Author(s):  
Amir Hussain ◽  
Ahsen Tahir ◽  
Zain Hussain ◽  
Zakariya Sheikh ◽  
Mandar Gogate ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
Vaccine Development ◽  
Public Attitudes ◽  
Public Attitude ◽  
Media Analysis ◽  
Public Confidence ◽  
The Us ◽  
The Uk

AbstractBackgroundGlobal efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern.MethodsOver 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural language processing and deep learning based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual reading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis.ResultsWe found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly.ConclusionsAI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.

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