JMIR infodemiology最新文献

{"title":"Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster-Based BERT Topic Modeling Approach.","authors":"Weiai Wayne Xu,&nbsp;Jean Marie Tshimula,&nbsp;Ève Dubé,&nbsp;Janice E Graham,&nbsp;Devon Greyson,&nbsp;Noni E MacDonald,&nbsp;Samantha B Meyer","doi":"10.2196/41198","DOIUrl":"https://doi.org/10.2196/41198","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has spotlighted the politicization of public health issues. A public health monitoring tool must be equipped to reveal a public health measure's political context and guide better interventions. In its current form, infoveillance tends to neglect identity and interest-based users, hence being limited in exposing how public health discourse varies by different political groups. Adopting an algorithmic tool to classify users and their short social media texts might remedy that limitation.</p><p><strong>Objective: </strong>We aimed to implement a new computational framework to investigate discourses and temporal changes in topics unique to different user clusters. The framework was developed to contextualize how web-based public health discourse varies by identity and interest-based user clusters. We used masks and mask wearing during the early stage of the COVID-19 pandemic in the English-speaking world as a case study to illustrate the application of the framework.</p><p><strong>Methods: </strong>We first clustered Twitter users based on their identities and interests as expressed through Twitter bio pages. Exploratory text network analysis reveals salient political, social, and professional identities of various user clusters. It then uses BERT Topic modeling to identify topics by the user clusters. It reveals how web-based discourse has shifted over time and varied by 4 user clusters: conservative, progressive, general public, and public health professionals.</p><p><strong>Results: </strong>This study demonstrated the importance of a priori user classification and longitudinal topical trends in understanding the political context of web-based public health discourse. The framework reveals that the political groups and the general public focused on the science of mask wearing and the partisan politics of mask policies. A populist discourse that pits citizens against elites and institutions was identified in some tweets. Politicians (such as Donald Trump) and geopolitical tensions with China were found to drive the discourse. It also shows limited participation of public health professionals compared with other users.</p><p><strong>Conclusions: </strong>We conclude by discussing the importance of a priori user classification in analyzing web-based discourse and illustrating the fit of BERT Topic modeling in identifying contextualized topics in short social media texts.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10402297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study.","authors":"Kai Luo,&nbsp;Yang Yang,&nbsp;Hock Hai Teo","doi":"10.2196/37331","DOIUrl":"https://doi.org/10.2196/37331","url":null,"abstract":"<p><strong>Background: </strong>Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-à-vis preprints and what makes them viral.</p><p><strong>Objective: </strong>This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators' and policy makers' decisions on how to promote reliable sharing of crucial pandemic science on social media.</p><p><strong>Methods: </strong>We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate.</p><p><strong>Results: </strong>Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists' participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications.</p><p><strong>Conclusions: </strong>Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9749104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10402298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users' Comments.","authors":"Philipp A Toussaint,&nbsp;Maximilian Renner,&nbsp;Sebastian Lins,&nbsp;Scott Thiebes,&nbsp;Ali Sunyaev","doi":"10.2196/38749","DOIUrl":"https://doi.org/10.2196/38749","url":null,"abstract":"<p><strong>Background: </strong>With direct-to-consumer (DTC) genetic testing enabling self-responsible access to novel information on ancestry, traits, or health, consumers often turn to social media for assistance and discussion. YouTube, the largest social media platform for videos, offers an abundance of DTC genetic testing-related videos. Nevertheless, user discourse in the comments sections of these videos is largely unexplored.</p><p><strong>Objective: </strong>This study aims to address the lack of knowledge concerning user discourse in the comments sections of DTC genetic testing-related videos on YouTube by exploring topics discussed and users' attitudes toward these videos.</p><p><strong>Methods: </strong>We employed a 3-step research approach. First, we collected metadata and comments of the 248 most viewed DTC genetic testing-related videos on YouTube. Second, we conducted topic modeling using word frequency analysis, bigram analysis, and structural topic modeling to identify topics discussed in the comments sections of those videos. Finally, we employed Bing (binary), National Research Council Canada (NRC) emotion, and 9-level sentiment analysis to identify users' attitudes toward these DTC genetic testing-related videos, as expressed in their comments.</p><p><strong>Results: </strong>We collected 84,082 comments from the 248 most viewed DTC genetic testing-related YouTube videos. With topic modeling, we identified 6 prevailing topics on (1) general genetic testing, (2) ancestry testing, (3) relationship testing, (4) health and trait testing, (5) ethical concerns, and (6) YouTube video reaction. Further, our sentiment analysis indicates strong positive emotions (anticipation, joy, surprise, and trust) and a neutral-to-positive attitude toward DTC genetic testing-related videos.</p><p><strong>Conclusions: </strong>With this study, we demonstrate how to identify users' attitudes on DTC genetic testing by examining topics and opinions based on YouTube video comments. Shedding light on user discourse on social media, our findings suggest that users are highly interested in DTC genetic testing and related social media content. Nonetheless, with this novel market constantly evolving, service providers, content providers, or regulatory authorities may still need to adapt their services to users' interests and desires.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9718455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Platform Effects on Public Health Communication: A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook.","authors":"Nic DePaula,&nbsp;Loni Hagen,&nbsp;Stiven Roytman,&nbsp;Dana Alnahass","doi":"10.2196/40198","DOIUrl":"https://doi.org/10.2196/40198","url":null,"abstract":"<p><strong>Background: </strong>Public health agencies widely adopt social media for health and risk communication. Moreover, different platforms have different affordances, which may impact the quality and nature of the messaging and how the public engages with the content. However, these platform effects are not often compared in studies of health and risk communication and not previously for the COVID-19 pandemic.</p><p><strong>Objective: </strong>This study measures the potential media effects of Twitter and Facebook on public health message design and engagement by comparing message elements and audience engagement in COVID-19-related posts by local, state, and federal public health agencies in the United States during the pandemic, to advance theories of public health messaging on social media and provide recommendations for tailored social media communication strategies.</p><p><strong>Methods: </strong>We retrieved all COVID-19-related posts from major US federal agencies related to health and infectious disease, all major state public health agencies, and selected local public health departments on Twitter and Facebook. A total of 100,785 posts related to COVID-19, from 179 different accounts of 96 agencies, were retrieved for the entire year of 2020. We adopted a framework of social media message elements to analyze the posts across Facebook and Twitter. For manual content analysis, we subsampled 1677 posts. We calculated the prevalence of various message elements across the platforms and assessed the statistical significance of differences. We also calculated and assessed the association between message elements with normalized measures of shares and likes for both Facebook and Twitter.</p><p><strong>Results: </strong>Distributions of message elements were largely similar across both sites. However, political figures (<i>P</i><.001), experts (<i>P</i>=.01), and nonpolitical personalities (<i>P</i>=.01) were significantly more present on Facebook posts compared to Twitter. Infographics (<i>P</i><.001), surveillance information (<i>P</i><.001), and certain multimedia elements (eg, hyperlinks, <i>P</i><.001) were more prevalent on Twitter. In general, Facebook posts received more (normalized) likes (0.19%) and (normalized) shares (0.22%) compared to Twitter likes (0.08%) and shares (0.05%). Elements with greater engagement on Facebook included expressives and collectives, whereas posts related to policy were more engaged with on Twitter. Science information (eg, scientific explanations) comprised 8.5% (73/851) of Facebook and 9.4% (78/826) of Twitter posts. Correctives of misinformation only appeared in 1.2% (11/851) of Facebook and 1.4% (12/826) of Twitter posts.</p><p><strong>Conclusions: </strong>In general, we find a data and policy orientation for Twitter messages and users and a local and personal orientation for Facebook, although also many similarities across platforms. Message elements that impact engagement are similar across pla","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9773105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10453850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Users' Modifications to Electronic Nicotine Delivery Systems: Content Analysis of YouTube Video Comments.","authors":"Yachao Li,&nbsp;David L Ashley,&nbsp;Lucy Popova","doi":"10.2196/38268","DOIUrl":"https://doi.org/10.2196/38268","url":null,"abstract":"<p><strong>Background: </strong>User modifications can alter the toxicity and addictiveness of electronic nicotine delivery systems (ENDSs). YouTube has been a major platform where ENDS users obtain and share information about ENDS modifications. Past research has examined the content and characteristics of ENDS modification videos.</p><p><strong>Objective: </strong>This study aims to analyze the video comments to understand the viewers' reactions to these videos.</p><p><strong>Methods: </strong>We identified 168 YouTube videos depicting ENDS modifications. Each video's top 20 most liked comments were retrieved. The final sample included 2859 comments. A content analysis identified major themes of the comment content.</p><p><strong>Results: </strong>Most comments were directed to creators and interacted with others: 952/2859 (33.30%) expressed appreciation, 135/2859 (4.72%) requested more videos, 462/2859 (16.16%) asked for clarification, and 67/2859 (2.34%) inquired about product purchases. In addition, comments mentioned viewers' experiences of ENDS modifications (430/2859, 15.04%) and tobacco use (167/2859, 5.84%); about 198/2859 (6.93%) also indicated intentions to modify ENDSs and 34/2859 (1.19%) mentioned that they were \"newbies.\" Moreover, comments included modification knowledge: 346/2859 (12.10%) provided additional information, 227/2859 (7.94%) mentioned newly learned knowledge, and 162/2859 (5.67%) criticized the videos. Furthermore, few comments mentioned the dangers of ENDS modifications (136/2859, 4.76%) and tobacco use (7/2859, 0.24%). Lastly, among the 15 comments explicitly mentioning regulations, 13/2859 (0.45%) were against and 2/2859 (0.07%) were supportive of regulations.</p><p><strong>Conclusions: </strong>The results indicated acceptance and popularity of ENDS modifications and suggested that the videos might motivate current and new users to alter their devices. Few comments mentioned the risks and regulations. Regulatory research and agencies should be aware of online ENDS modification information and understand its impacts on users.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9387347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40433659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding.","authors":"Loni Hagen,&nbsp;Ashley Fox,&nbsp;Heather O'Leary,&nbsp;DeAndre Dyson,&nbsp;Kimberly Walker,&nbsp;Cecile A Lengacher,&nbsp;Raquel Hernandez","doi":"10.2196/34231","DOIUrl":"https://doi.org/10.2196/34231","url":null,"abstract":"<p><strong>Background: </strong>Since COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers have indicated a polarized social media presence contributing to the spread of mis- or disinformation as being responsible for these growing partisan gaps in uptake.</p><p><strong>Objective: </strong>The major aim of this study was to investigate the role of influential actors in the context of the community structures and discourse related to COVID-19 vaccine conversations on Twitter that emerged prior to the vaccine rollout to the general population and discuss implications for vaccine promotion and policy.</p><p><strong>Methods: </strong>We collected tweets on COVID-19 between July 1, 2020, and July 31, 2020, a time when attitudes toward the vaccines were forming but before the vaccines were widely available to the public. Using network analysis, we identified different naturally emerging Twitter communities based on their internal information sharing. A PageRank algorithm was used to quantitively measure the level of \"influentialness\" of Twitter accounts and identifying the \"influencers,\" followed by coding them into different actor categories. Inductive coding was conducted to describe discourses shared in each of the 7 communities.</p><p><strong>Results: </strong>Twitter vaccine conversations were highly polarized, with different actors occupying separate \"clusters.\" The antivaccine cluster was the most densely connected group. Among the 100 most influential actors, medical experts were outnumbered both by partisan actors and by activist vaccine skeptics or conspiracy theorists. Scientists and medical actors were largely absent from the conservative network, and antivaccine sentiment was especially salient among actors on the political right. Conversations related to COVID-19 vaccines were highly polarized along partisan lines, with \"trust\" in vaccines being manipulated to the political advantage of partisan actors.</p><p><strong>Conclusions: </strong>These findings are informative for designing improved vaccine information communication strategies to be delivered on social media especially by incorporating influential actors. Although polarization and echo chamber effect are not new in political conversations in social media, it was concerning to observe these in health conversations on COVID-19 vaccines during the vaccine development process.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9254747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40491043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early detection of fraudulent COVID-19 products from Twitter chatter","authors":"A. Sarker, S. Lakamana, R. Liao, A. Abbas, Y.-C. Yang, M. Al-garadi","doi":"10.1101/2022.05.09.22274776","DOIUrl":"https://doi.org/10.1101/2022.05.09.22274776","url":null,"abstract":"Social media have served as lucrative platforms for misinformation and for promoting fraudulent products for the treatment, testing and prevention of COVID-19. This has resulted in the issuance of many warning letters by the United States Food and Drug Administration (FDA). While social media continue to serve as the primary platform for the promotion of such fraudulent products, they also present the opportunity to identify these products early by employing effective social media mining methods. In this study, we employ natural language processing and time series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We utilized an anomaly detection method on streaming COVID-19-related Twitter data to detect potentially anomalous increases in mentions of fraudulent products. Our unsupervised approach detected 34/44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6/44 (13.6%) within a week following the corresponding FDA letters. Our proposed method is simple, effective and easy to deploy, and do not require high performance computing machinery unlike deep neural network-based methods.","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46452234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Literacy, Equity, and Communication in the COVID-19 Era of Misinformation: Emergence of Health Information Professionals in Infodemic Management.","authors":"Ramona Kyabaggu, Deneice Marshall, Patience Ebuwei, Uche Ikenyei","doi":"10.2196/35014","DOIUrl":"10.2196/35014","url":null,"abstract":"<p><p>The health information management (HIM) field's contribution to health care delivery is invaluable in a pandemic context where the need for accurate diagnoses will hasten responsive, evidence-based decision-making. The COVID-19 pandemic offers a unique opportunity to transform the practice of HIM and bring more awareness to the role that frontline workers play behind the scenes in safeguarding reliable, comprehensive, accurate, and timely health information. This transformation will support future research, utilization management, public health surveillance, and forecasting and enable key stakeholders to plan and ensure equitable health care resource allocation, especially for the most vulnerable populations. In this paper, we juxtapose critical health literacy, public policy, and HIM perspectives to understand the COVID-19 infodemic and new opportunities for HIM in infodemic management.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9352249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"US Black Maternal Health Advocacy Topics and Trends on Twitter: Temporal Infoveillance Study","authors":"D. Grigsby-Toussaint, Ashley Champagne, Justin Uhr, Elizabeth Silva, Madeline Noh, Adam Bradley, Patrick Rashleigh","doi":"10.2196/30885","DOIUrl":"https://doi.org/10.2196/30885","url":null,"abstract":"Background Black women in the United States disproportionately suffer adverse pregnancy and birth outcomes compared to White women. Economic adversity and implicit bias during clinical encounters may lead to physiological responses that place Black women at higher risk for adverse birth outcomes. The novel coronavirus disease of 2019 (COVID-19) further exacerbated this risk, as safety protocols increased social isolation in clinical settings, thereby limiting opportunities to advocate for unbiased care. Twitter, 1 of the most popular social networking sites, has been used to study a variety of issues of public interest, including health care. This study considers whether posts on Twitter accurately reflect public discourse during the COVID-19 pandemic and are being used in infodemiology studies by public health experts. Objective This study aims to assess the feasibility of Twitter for identifying public discourse related to social determinants of health and advocacy that influence maternal health among Black women across the United States and to examine trends in sentiment between 2019 and 2020 in the context of the COVID-19 pandemic. Methods Tweets were collected from March 1 to July 13, 2020, from 21 organizations and influencers and from 4 hashtags that focused on Black maternal health. Additionally, tweets from the same organizations and hashtags were collected from the year prior, from March 1 to July 13, 2019. Twint, a Python programming library, was used for data collection and analysis. We gathered the text of approximately 17,000 tweets, as well as all publicly available metadata. Topic modeling and k-means clustering were used to analyze the tweets. Results A variety of trends were observed when comparing the 2020 data set to the 2019 data set from the same period. The percentages listed for each topic are probabilities of that topic occurring in our corpus. In our topic models, tweets on reproductive justice, maternal mortality crises, and patient care increased by 67.46% in 2020 versus 2019. Topics on community, advocacy, and health equity increased by over 30% in 2020 versus 2019. In contrast, tweet topics that decreased in 2020 versus 2019 were as follows: tweets on Medicaid and medical coverage decreased by 27.73%, and discussions about creating space for Black women decreased by just under 30%. Conclusions The results indicate that the COVID-19 pandemic may have spurred an increased focus on advocating for improved reproductive health and maternal health outcomes among Black women in the United States. Further analyses are needed to capture a longer time frame that encompasses more of the pandemic, as well as more diverse voices to confirm the robustness of the findings. We also concluded that Twitter is an effective source for providing a snapshot of relevant topics to guide Black maternal health advocacy efforts.","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44748924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Frames of the COVID-19 Infodemic: Thematic Analysis of Misinformation Stories Across Media.","authors":"Ehsan Mohammadi, Iman Tahamtan, Yazdan Mansourian, Holly Overton","doi":"10.2196/33827","DOIUrl":"10.2196/33827","url":null,"abstract":"<p><strong>Background: </strong>The word \"infodemic\" refers to the deluge of false information about an event, and it is a global challenge for today's society. The sheer volume of misinformation circulating during the COVID-19 pandemic has been harmful to people around the world. Therefore, it is important to study different aspects of misinformation related to the pandemic.</p><p><strong>Objective: </strong>This paper aimed to identify the main subthemes related to COVID-19 misinformation on various platforms, from traditional outlets to social media. This paper aimed to place these subthemes into categories, track the changes, and explore patterns in prevalence, over time, across different platforms and contexts.</p><p><strong>Methods: </strong>From a theoretical perspective, this research was rooted in framing theory; it also employed thematic analysis to identify the main themes and subthemes related to COVID-19 misinformation. The data were collected from 8 fact-checking websites that formed a sample of 127 pieces of false COVID-19 news published from January 1, 2020 to March 30, 2020.</p><p><strong>Results: </strong>The findings revealed 4 main themes (attribution, impact, protection and solutions, and politics) and 19 unique subthemes within those themes related to COVID-19 misinformation. Governmental and political organizations (institutional level) and administrators and politicians (individual level) were the 2 most frequent subthemes, followed by origination and source, home remedies, fake statistics, treatments, drugs, and pseudoscience, among others. Results indicate that the prevalence of misinformation subthemes had altered over time between January 2020 and March 2020. For instance, false stories about the origin and source of the virus were frequent initially (January). Misinformation regarding home remedies became a prominent subtheme in the middle (February), while false information related to government organizations and politicians became popular later (March). Although conspiracy theory web pages and social media outlets were the primary sources of misinformation, surprisingly, results revealed trusted platforms such as official government outlets and news organizations were also avenues for creating COVID-19 misinformation.</p><p><strong>Conclusions: </strong>The identified themes in this study reflect some of the information attitudes and behaviors, such as denial, uncertainty, consequences, and solution-seeking, that provided rich information grounds to create different types of misinformation during the COVID-19 pandemic. Some themes also indicate that the application of effective communication strategies and the creation of timely content were used to persuade human minds with false stories in different phases of the crisis. The findings of this study can be beneficial for communication officers, information professionals, and policy makers to combat misinformation in future global health crises or related events.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9363930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}