Social media and public perception as core aspect of public health: The cautionary case of @realdonaldtrump and COVID-19.
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ABSTRACT:
SUBMITTER: Fuentes A
PROVIDER: S-EPMC8099101 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The unpredictability and unknowns surrounding the ongoing coronavirus disease (COVID-19) pandemic have led to an unprecedented consequence taking a heavy toll on the lives and economies of all countries. There have been efforts to predict COVID-19 case counts (CCC) using epidemiological data and numerical tokens online, which may allow early preventive measures to slow the spread of the disease. In this paper, we use state-of-the-art natural language processing (NLP) algorithms to numerically encode COVID-19 related tweets originated from eight cities in the United States and predict city-specific CCC up to eight days in the future. A city-embedding is proposed to obtain a time series representation of daily tweets posted from a city, which is then used to predict case counts using a custom long-short term memory (LSTM) model. The universal sentence encoder yields the best normalized root mean squared error (NRMSE) 0.090 (0.039), averaged across all cities in predicting CCC six days in the future. The R 2 scores in predicting CCC are more than 0.70 and often over 0.8, which suggests a strong correlation between the actual and our model predicted CCC values. Our analyses show that the NRMSE and R 2 scores are consistently robust across different cities and different numbers of time steps in time series data. Results show that the LSTM model can learn the mapping between the NLP-encoded tweet semantics and the case counts, which infers that social media text can be directly mined to identify the future course of the pandemic.
| S-EPMC9490453 | biostudies-literature
Project description:OBJECTIVE:Social distancing policies are key in curtailing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We studied public perception of social distancing through organic, large-scale discussion on Twitter. DESIGN:Retrospective cross-sectional study. METHODS:Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine-learning models, and we conducted a sentiment analysis to identify emotions and polarity. We evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. RESULTS:We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0-0.6) with ~30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half of tweets (50.4%) primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (ie, joy), concerns about food insecurity and quarantine effects (ie, fear), and unpredictability of coronavirus disease 2019 (COVID-19) and its implications (ie, surprise). CONCLUSIONS:Considering the positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms, we concluded that Twitter users generally supported social distancing in the early stages of their implementation.
| S-EPMC7450231 | biostudies-literature
Project description:Social media has been used as data resource in a growing number of health-related research. The objectives of this study were to identify content volume and sentiment polarity of social media records relevant to healthcare services in China. A list of the key words of healthcare services were used to extract data from WeChat and Qzone, between June 2017 and September 2017. The data were put into a corpus, where content analyses were performed using Tencent natural language processing (NLP). The final corpus contained approximately 29 million records. Records on patient safety were the most frequently mentioned topic (approximately 8.73 million, 30.1% of the corpus), with the contents on humanistic care having received the least social media references (0.43 Million, 1.5%). Sentiment analyses showed 36.1%, 16.4%, and 47.4% of positive, neutral, and negative emotions, respectively. The doctor-patient relationship category had the highest proportion of negative contents (74.9%), followed by service efficiency (59.5%), and nursing service (53.0%). Neutral disposition was found to be the highest (30.4%) in the contents on appointment-booking services. This study added evidence to the magnitude and direction of public perceptions on healthcare services in China's hospital and pointed to the possibility of monitoring healthcare service improvement, using readily available data in social media.
| S-EPMC6479867 | biostudies-literature
Project description:Rising COVID-19 cases in Canada in early 2021, coupled with pervasive mis- and disinformation, demonstrate the critical relationship between effective crisis communication, trust, and risk protective measure adherence by the public. Trust in crisis communication is affected by the communication's characteristics including transparency, timeliness, empathy, and clarity, as well as the source and communication channels used. Crisis communication occurs in a rhetorical arena where various actors, including public health, news media, and the public, are co-producing and responding to messages. Rhetorical arenas must be monitored to assess the acceptance of messaging. The quality and content of Canadian public health and news media crisis communication on Facebook were evaluated to understand the use of key guiding principles of effective crisis communication, the focus of the communication, and subsequent public emotional response to included posts. Four hundred and thirty-eight posts and 26,774 anonymized comments were collected and analyzed. Overall, the guiding principles for effective crisis communication were inconsistently applied and combined. A limited combination of guiding principles, especially those that demonstrate trustworthiness, was likely driving the negative sentiment uncovered in the comments. Public health and news media should use the guiding principles consistently to increase positive sentiment and build trust among followers.
| S-EPMC8345485 | biostudies-literature
Project description:Artificial intelligence can enable real-time analysis of public attitudes, and their demographic determinants, from social media and linked Web platforms. This analysis offers the opportunity to track changing public sentiments and develop proactive two-way communication strategies. In the context of Covid-19, an iterative learning cycle can help maximize vaccine uptake across demographic communities by identifying and addressing unforeseen areas of public concern. Summary As vaccines against Covid-19 are rolled out, there is a pressing need to better understand and monitor public sentiments and address the concerns of vaccine sceptics. This urgency has been exacerbated by the current wave of the global pandemic and growing pressures on health services.1 The Royal Society reviewed the behavioral aspects of vaccine uptake and misinformation and identified the need for further research into five factors: confidence in the vaccines’ efficacy and safety; complacency; convenience; sociodemographic variation; and sources of information.2 The survey advocated the need for public dialogue around vaccine concerns and hesitancy3 to replace the current passive communication strategies that tend to focus narrowly on “infodemic” challenges,2 because genuine knowledge voids are being filled by misinformation. Artificial intelligence (AI) can enable real-time analysis of public attitudes, and their demographic determinants, from social media and linked Web platforms. This analysis offers the opportunity to track changing public sentiments and develop proactive two-way communication strategies. In particular, an iterative learning cycle can help identify unforeseen areas of public concern as well as potential barriers and facilitators for required interventions, prior to their implementation, to maximize vaccine uptake across demographic communities.
| S-EPMC7864479 | biostudies-literature
Project description:BackgroundThe emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2.ObjectiveIn this study, we sought to explore the suitability of artificial intelligence (AI)-enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom.MethodsWe extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19-related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app-related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning-based approaches.ResultsOverall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology.ConclusionsVariations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.
| S-EPMC8130818 | biostudies-literature
Project description:BACKGROUND:High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public's views on medical AI. Currently, the opinions of the general public on this matter remain unclear. OBJECTIVE:The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. METHODS:Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. RESULTS:Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors. CONCLUSIONS:Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients' emotional needs instead of focusing merely on technical issues.
| S-EPMC7385634 | biostudies-literature
Project description:Background: The current development of vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented. Little is known, however, about the nuanced public opinions on the vaccines on social media. Methods: We adopt a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. After feature inference and opinion mining, 10,945 unique Twitter users are included in the study population. Multinomial logistic regression and counterfactual analysis are conducted. Results: Socioeconomically disadvantaged groups are more likely to hold polarized opinions on coronavirus disease 2019 (COVID-19) vaccines either pro-vaccine ( B=0.40,SE=0.08,P<.001,OR=1.49;95%CI=[1.26,1.75] ) or anti-vaccine ( B=0.52,SE=0.06,P<.001,OR=1.69;95%CI=[1.49,1.91] ). People who have the worst personal pandemic experience are more likely to hold the anti-vaccine opinion ( B=-0.18,SE=0.04,P<.001,OR=0.84;95%CI=[0.77,0.90] ). The U.S. public is most concerned about the safety, effectiveness, and political issues regarding vaccines for COVID-19, and improving personal pandemic experience increases the vaccine acceptance level. Conclusion: Opinion on COVID-19 vaccine uptake varies across people of different characteristics.
| S-EPMC8384764 | biostudies-literature
Project description:BackgroundSocial media platforms have long been recognised as major disseminators of health misinformation. Many previous studies have found a negative association between health-protective behaviours and belief in the specific form of misinformation popularly known as 'conspiracy theory'. Concerns have arisen regarding the spread of COVID-19 conspiracy theories on social media.MethodsThree questionnaire surveys of social media use, conspiracy beliefs and health-protective behaviours with regard to COVID-19 among UK residents were carried out online, one using a self-selecting sample (N = 949) and two using stratified random samples from a recruited panel (N = 2250, N = 2254).ResultsAll three studies found a negative relationship between COVID-19 conspiracy beliefs and COVID-19 health-protective behaviours, and a positive relationship between COVID-19 conspiracy beliefs and use of social media as a source of information about COVID-19. Studies 2 and 3 also found a negative relationship between COVID-19 health-protective behaviours and use of social media as a source of information, and Study 3 found a positive relationship between health-protective behaviours and use of broadcast media as a source of information.ConclusionsWhen used as an information source, unregulated social media may present a health risk that is partly but not wholly reducible to their role as disseminators of health-related conspiracy beliefs.
| S-EPMC7298098 | biostudies-literature
Project description:IntroductionDespite the potential of social media to influence public health and generate insights, the process of monitoring and analyzing the dissemination of health care messages on social media has been described as difficult and in need of improvement.ObjectivesThe objective of this study was to describe and demonstrate a reproducible methodology for cataloging and analyzing health care-related social media comments and provide insight into how clinicians and members of the general public respond to health care messaging on social media.MethodsWe collected social media comments related to the American Dental Association's 2016 "Evidence-Based Clinical Practice Guideline for the Use of Pit-and-Fissure Sealants" between April 10, 2017, and October 31, 2017, from Facebook, Twitter, LinkedIn, Reddit, and online message boards for the New York Times, FiveThirtyEight, and Dentaltown. Using data provided in the comments, we conducted engagement analysis as well as content, network, and sentiment analysis across 8 categories.ResultsWe collected 671 comments. Among our findings, Facebook (472 of 671) was the most popular platform among commentators; almost half of all comments (335 of 671) aligned with the recommendations of the 2016 American Dental Association sealants guideline; clinicians were more likely than the general public to like a comment that suggested an improvement to the guideline; and >75% of comments (521 of 671) were supported by anecdotal evidence.ConclusionAs the prevalence of anecdotes on social media suggests, the likelihood of falsehoods spreading on social media is high. Insights gleaned from the methodology described in this research could help combat the spread of such misinformation by providing disseminators of health care messaging with insight into their target audiences. Armed with this knowledge, disseminators can craft health care messages that more effectively engage clinicians and the general public.Knowledge transfer statementThe methodology used in this research provides a reproducible strategy for tracking social media engagement with health care messages. Engagement results can assist future delivery of health care messages to key stakeholders and ensure better implementation and adoption of these communications.
| S-EPMC6918023 | biostudies-literature