JMIR infodemiology最新文献

{"title":"Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights.","authors":"Hannah Stevens, Muhammad Ehab Rasul, Yoo Jung Oh","doi":"10.2196/37635","DOIUrl":"10.2196/37635","url":null,"abstract":"<p><strong>Background: </strong>Despite vaccine availability, vaccine hesitancy has inhibited public health officials' efforts to mitigate the COVID-19 pandemic in the United States. Although some US elected officials have responded by issuing vaccine mandates, others have amplified vaccine hesitancy by broadcasting messages that minimize vaccine efficacy. The politically polarized nature of COVID-19 information on social media has given rise to incivility, wherein health attitudes often hinge more on political ideology than science.</p><p><strong>Objective: </strong>To the best of our knowledge, incivility has not been studied in the context of discourse regarding COVID-19 vaccines and mandates. Specifically, there is little focus on the psychological processes that elicit uncivil vaccine discourse and behaviors. Thus, we investigated 3 psychological processes theorized to predict discourse incivility-namely, anxiety, anger, and sadness.</p><p><strong>Methods: </strong>We used 2 different natural language processing approaches: (1) the Linguistic Inquiry and Word Count computational tool and (2) the Google Perspective application programming interface (API) to analyze a data set of 8014 tweets containing terms related to COVID-19 vaccine mandates from September 14, 2021, to October 1, 2021. To collect the tweets, we used the Twitter API Tweet Downloader Tool (version 2). Subsequently, we filtered through a data set of 375,000 vaccine-related tweets using keywords to extract tweets explicitly focused on vaccine mandates. We relied on the Linguistic Inquiry and Word Count computational tool to measure the valence of linguistic anger, sadness, and anxiety in the tweets. To measure dimensions of post incivility, we used the Google Perspective API.</p><p><strong>Results: </strong>This study resolved discrepant operationalizations of incivility by introducing incivility as a multifaceted construct and explored the distinct emotional processes underlying 5 dimensions of discourse incivility. The findings revealed that 3 types of emotions-anxiety, anger, and sadness-were uniquely associated with dimensions of incivility (eg, toxicity, severe toxicity, insult, profanity, threat, and identity attacks). Specifically, the results showed that anger was significantly positively associated with all dimensions of incivility (all <i>P</i><.001), whereas sadness was significantly positively related to threat (<i>P</i>=.04). Conversely, anxiety was significantly negatively associated with identity attack (<i>P</i>=.03) and profanity (<i>P</i>=.02).</p><p><strong>Conclusions: </strong>The results suggest that our multidimensional approach to incivility is a promising alternative to understanding and intervening in the psychological processes underlying uncivil vaccine discourse. Understanding specific emotions that can increase or decrease incivility such as anxiety, anger, and sadness can enable researchers and public health professionals to develop effective inte","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e37635"},"PeriodicalIF":3.5,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9704987","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":"COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic.","authors":"Nikhil Kolluri, Yunong Liu, Dhiraj Murthy","doi":"10.2196/38756","DOIUrl":"10.2196/38756","url":null,"abstract":"<p><strong>Background: </strong>The volume of COVID-19-related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning-based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19-related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed.</p><p><strong>Objective: </strong>The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response.</p><p><strong>Methods: </strong>We evaluated three strategies for training a machine-learning model to determine the highest model performance: (1) COVID-19-related fact-checked data only, (2) general fact-checked data only, and (3) combined COVID-19 and general fact-checked data. We created two COVID-19-related misinformation data sets from fact-checked \"false\" content combined with programmatically retrieved \"true\" content. The first set contained ~7000 entries from July to August 2020, and the second contained ~31,000 entries from January 2020 to June 2022. We crowdsourced 31,441 votes to human label the first data set.</p><p><strong>Results: </strong>The models achieved an accuracy of 96.55% and 94.56% on the first and second external validation data set, respectively. Our best-performing model was developed using COVID-19-specific content. We were able to successfully develop combined models that outperformed human votes of misinformation. Specifically, when we blended our model predictions with human votes, the highest accuracy we achieved on the first external validation data set was 99.1%. When we considered outputs where the machine-learning model agreed with human votes, we achieved accuracies up to 98.59% on the first validation data set. This outperformed human votes alone with an accuracy of only 73%.</p><p><strong>Conclusions: </strong>External validation accuracies of 96.55% and 94.56% are evidence that machine learning can produce superior results for the difficult task of classifying the veracity of COVID-19 content. Pretrained language models performed best when fine-tuned on a topic-specific data set, while other models achieved their best accuracy when fine-tuned on a combination of topic-specific and general-topic data sets. Crucially, our study found that blended models, trained/fine-tuned on general-topic content with crowdsourced data, improved our models' accuracies up to 99.7%. The successful use of crowdsourced data can increase the accuracy of models in situations when expert-labeled data are scarce. The 98.59% accuracy on a \"high-confidence\" subsection comprised of machine-learned and human labels sugges","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e38756"},"PeriodicalIF":3.5,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9733183","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":"Media Data and Vaccine Hesitancy: Scoping Review.","authors":"Jason Dean-Chen Yin","doi":"10.2196/37300","DOIUrl":"10.2196/37300","url":null,"abstract":"<p><strong>Background: </strong>Media studies are important for vaccine hesitancy research, as they analyze how the media shapes risk perceptions and vaccine uptake. Despite the growth in studies in this field owing to advances in computing and language processing and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology.</p><p><strong>Objective: </strong>This review aimed to identify and illustrate the media platforms and methods used to study vaccine hesitancy and how they build or contribute to the study of the media's influence on vaccine hesitancy and public health.</p><p><strong>Methods: </strong>This study followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A search was conducted on PubMed and Scopus for any studies that used media data (social media or traditional media), had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, or stance), were written in English, and were published after 2010. Studies were screened by only 1 reviewer and extracted for media platform, analysis method, the theoretical models used, and outcomes.</p><p><strong>Results: </strong>In total, 125 studies were included, of which 71 (56.8%) used traditional research methods and 54 (43.2%) used computational methods. Of the traditional methods, most used content analysis (43/71, 61%) and sentiment analysis (21/71, 30%) to analyze the texts. The most common platforms were newspapers, print media, and web-based news. The computational methods mostly used sentiment analysis (31/54, 57%), topic modeling (18/54, 33%), and network analysis (17/54, 31%). Fewer studies used projections (2/54, 4%) and feature extraction (1/54, 2%). The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. The following five major categories of studies arose: antivaccination themes centered on the distrust of institutions, civil liberties, misinformation, conspiracy theories, and vaccine-specific concerns; provaccination themes centered on ensuring vaccine safety using scientific literature; framing being important and health professionals and personal stories having the largest impact on shaping vaccine opinion; the coverage of vaccination-related data mostly identifying negative vaccine content and revealing deeply fractured vaccine communities and echo chambers; and the public reacting to and focusing on certain signals-in particular cases, deaths, and scandals-which suggests a more volatile period for the spread of information.</p><p><strong>Conclusions: </strong>The heterogeneity in the use of media to study vaccines can be better consolidated through theoretical grounding. Areas of suggested research include understanding how trust in institutions is asso","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e37300"},"PeriodicalIF":3.5,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987198/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9421212","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":"Implicit Incentives Among Reddit Users to Prioritize Attention Over Privacy and Reveal Their Faces When Discussing Direct-to-Consumer Genetic Test Results: Topic and Attention Analysis.","authors":"Yongtai Liu, Zhijun Yin, Zhiyu Wan, Chao Yan, Weiyi Xia, Congning Ni, Ellen Wright Clayton, Yevgeniy Vorobeychik, Murat Kantarcioglu, Bradley A Malin","doi":"10.2196/35702","DOIUrl":"10.2196/35702","url":null,"abstract":"<p><strong>Background: </strong>As direct-to-consumer genetic testing services have grown in popularity, the public has increasingly relied upon online forums to discuss and share their test results. Initially, users did so anonymously, but more recently, they have included face images when discussing their results. Various studies have shown that sharing images on social media tends to elicit more replies. However, users who do this forgo their privacy. When these images truthfully represent a user, they have the potential to disclose that user's identity.</p><p><strong>Objective: </strong>This study investigates the face image sharing behavior of direct-to-consumer genetic testing users in an online environment to determine if there exists an association between face image sharing and the attention received from other users.</p><p><strong>Methods: </strong>This study focused on r/23andme, a subreddit dedicated to discussing direct-to-consumer genetic testing results and their implications. We applied natural language processing to infer the themes associated with posts that included a face image. We applied a regression analysis to characterize the association between the attention that a post received, in terms of the number of comments, the karma score (defined as the number of upvotes minus the number of downvotes), and whether the post contained a face image.</p><p><strong>Results: </strong>We collected over 15,000 posts from the r/23andme subreddit, published between 2012 and 2020. Face image posting began in late 2019 and grew rapidly, with over 800 individuals revealing their faces by early 2020. The topics in posts including a face were primarily about sharing, discussing ancestry composition, or sharing family reunion photos with relatives discovered via direct-to-consumer genetic testing. On average, posts including a face image received 60% (5/8) more comments and had karma scores 2.4 times higher than other posts.</p><p><strong>Conclusions: </strong>Direct-to-consumer genetic testing consumers in the r/23andme subreddit are increasingly posting face images and testing reports on social platforms. The association between face image posting and a greater level of attention suggests that people are forgoing their privacy in exchange for attention from others. To mitigate this risk, platform organizers and moderators could inform users about the risk of posting face images in a direct, explicit manner to make it clear that their privacy may be compromised if personal images are shared.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e35702"},"PeriodicalIF":3.5,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9581050","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":"Web-Based Perspectives of Deemed Consent Organ Donation Legislation in Nova Scotia: Thematic Analysis of Commentary in Facebook Groups.","authors":"Alessandro R Marcon,&nbsp;Darren N Wagner,&nbsp;Carly Giles,&nbsp;Cynthia Isenor","doi":"10.2196/38242","DOIUrl":"https://doi.org/10.2196/38242","url":null,"abstract":"<p><strong>Background: </strong>The Canadian province of Nova Scotia recently became the first jurisdiction in North America to implement deemed consent organ donation legislation. Changing the consent models constituted one aspect of a larger provincial program to increase organ and tissue donation and transplantation rates. Deemed consent legislation can be controversial among the public, and public participation is integral to the successful implementation of the program.</p><p><strong>Objective: </strong>Social media constitutes key spaces where people express opinions and discuss topics, and social media discourse can influence public perceptions. This project aimed to examine how the public in Nova Scotia responded to legislative changes in Facebook groups.</p><p><strong>Methods: </strong>Using Facebook's search engine, we searched for posts in public Facebook groups using the terms \"deemed consent,\" \"presumed consent,\" \"opt out,\" or \"organ donation\" and \"Nova Scotia,\" appearing from January 1, 2020, to May 1, 2021. The finalized data set included 2337 comments on 26 relevant posts in 12 different public Nova Scotia-based Facebook groups. We conducted thematic and content analyses of the comments to determine how the public responded to the legislative changes and how the participants interacted with one another in the discussions.</p><p><strong>Results: </strong>Our thematic analysis revealed principal themes that supported and critiqued the legislation, raised specific issues, and reflected on the topic from a neutral perspective. Subthemes showed individuals presenting perspectives through a variety of themes, including compassion, anger, frustration, mistrust, and a range of argumentative tactics. The comments included personal narratives, beliefs about the government, altruism, autonomy, misinformation, and reflections on religion and death. Content analysis revealed that Facebook users reacted to popular comments with \"likes\" more than other reactions. Comments with the most reactions included both negative and positive perspectives about the legislation. Personal donation and transplantation success stories, as well as attempts to correct misinformation, were some of the most \"liked\" positive comments.</p><p><strong>Conclusions: </strong>The findings provide key insights into perspectives of individuals from Nova Scotia on deemed consent legislation, as well as organ donation and transplantation broadly. The insights derived from this analysis can contribute to public understanding, policy creation, and public outreach efforts that might occur in other jurisdictions considering the enactment of similar legislation.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e38242"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9718458","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":"Confounding Effect of Undergraduate Semester-Driven \"Academic\" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration.","authors":"Timber Gillis,&nbsp;Scott Garrison","doi":"10.2196/34464","DOIUrl":"https://doi.org/10.2196/34464","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year-driven internet search patterns of health care students.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to (1) demonstrate that artificial \"academic cycling\" of Google Trends' search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term [\"Staphylococcus\"], for which academic cycling explained 73.8% of the variability in search volume (using the squared Spearman rank correlation coefficient, &lt;i&gt;P&lt;/i&gt;&lt;.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) [\"Aeromonas\" + \"Plesiomonas\"] (nosocomial infections that were searched for more frequently during the summer), (2) [\"Ehrlichia\"] (a tick-borne pathogen that was searched for more frequently during late spring), (3) [\"Moraxella\"] and [\"Haemophilus\"] (respiratory infections that were searched for more frequently during late winter), (4) [\"Legionella\"] (searched for more frequently during midsummer), and (5) [\"Vibrio\"] (which spiked for 2 months during midsummer). The terms [\"myocardial infarction\"] and [\"hypertension\"] lacked any obvious seasonal cycling after filtering, whereas [\"depression\"] maintained an annual cycling pattern.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends' internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to f","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e34464"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9733181","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":"Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study.","authors":"Kiki Ferawati,&nbsp;Kongmeng Liew,&nbsp;Eiji Aramaki,&nbsp;Shoko Wakamiya","doi":"10.2196/39504","DOIUrl":"https://doi.org/10.2196/39504","url":null,"abstract":"<p><strong>Background: </strong>The year 2021 was marked by vaccinations against COVID-19, which spurred wider discussion among the general population, with some in favor and some against vaccination. Twitter, a popular social media platform, was instrumental in providing information about the COVID-19 vaccine and has been effective in observing public reactions. We focused on tweets from Japan and Indonesia, 2 countries with a large Twitter-using population, where concerns about side effects were consistently stated as a strong reason for vaccine hesitancy.</p><p><strong>Objective: </strong>This study aimed to investigate how Twitter was used to report vaccine-related side effects and to compare the mentions of these side effects from 2 messenger RNA (mRNA) vaccine types developed by Pfizer and Moderna, in Japan and Indonesia.</p><p><strong>Methods: </strong>We obtained tweet data from Twitter using Japanese and Indonesian keywords related to COVID-19 vaccines and their side effects from January 1, 2021, to December 31, 2021. We then removed users with a high frequency of tweets and merged the tweets from multiple users as a single sentence to focus on user-level analysis, resulting in a total of 214,165 users (Japan) and 12,289 users (Indonesia). Then, we filtered the data to select tweets mentioning Pfizer or Moderna only and removed tweets mentioning both. We compared the side effect counts to the public reports released by Pfizer and Moderna. Afterward, logistic regression models were used to compare the side effects for the Pfizer and Moderna vaccines for each country.</p><p><strong>Results: </strong>We observed some differences in the ratio of side effects between the public reports and tweets. Specifically, fever was mentioned much more frequently in tweets than would be expected based on the public reports. We also observed differences in side effects reported between Pfizer and Moderna vaccines from Japan and Indonesia, with more side effects reported for the Pfizer vaccine in Japanese tweets and more side effects with the Moderna vaccine reported in Indonesian tweets.</p><p><strong>Conclusions: </strong>We note the possible consequences of vaccine side effect surveillance on Twitter and information dissemination, in that fever appears to be over-represented. This could be due to fever possibly having a higher severity or measurability, and further implications are discussed.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e39504"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9578292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9349248","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":"Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study.","authors":"Sinan Erturk,&nbsp;Georgie Hudson,&nbsp;Sonja M Jansli,&nbsp;Daniel Morris,&nbsp;Clarissa M Odoi,&nbsp;Emma Wilson,&nbsp;Angela Clayton-Turner,&nbsp;Vanessa Bray,&nbsp;Gill Yourston,&nbsp;Andrew Cornwall,&nbsp;Nicholas Cummins,&nbsp;Til Wykes,&nbsp;Sagar Jilka","doi":"10.2196/36871","DOIUrl":"https://doi.org/10.2196/36871","url":null,"abstract":"<p><strong>Background: </strong>Dementia misconceptions on Twitter can have detrimental or harmful effects. Machine learning (ML) models codeveloped with carers provide a method to identify these and help in evaluating awareness campaigns.</p><p><strong>Objective: </strong>This study aimed to develop an ML model to distinguish between misconceptions and neutral tweets and to develop, deploy, and evaluate an awareness campaign to tackle dementia misconceptions.</p><p><strong>Methods: </strong>Taking 1414 tweets rated by carers from our previous work, we built 4 ML models. Using a 5-fold cross-validation, we evaluated them and performed a further blind validation with carers for the best 2 ML models; from this blind validation, we selected the best model overall. We codeveloped an awareness campaign and collected pre-post campaign tweets (N=4880), classifying them with our model as misconceptions or not. We analyzed dementia tweets from the United Kingdom across the campaign period (N=7124) to investigate how current events influenced misconception prevalence during this time.</p><p><strong>Results: </strong>A random forest model best identified misconceptions with an accuracy of 82% from blind validation and found that 37% of the UK tweets (N=7124) about dementia across the campaign period were misconceptions. From this, we could track how the prevalence of misconceptions changed in response to top news stories in the United Kingdom. Misconceptions significantly rose around political topics and were highest (22/28, 79% of the dementia tweets) when there was controversy over the UK government allowing to continue hunting during the COVID-19 pandemic. After our campaign, there was no significant change in the prevalence of misconceptions.</p><p><strong>Conclusions: </strong>Through codevelopment with carers, we developed an accurate ML model to predict misconceptions in dementia tweets. Our awareness campaign was ineffective, but similar campaigns could be enhanced through ML to respond to current events that affect misconceptions in real time.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e36871"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9421213","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":"Social Listening to Enhance Access to Appropriate Pandemic Information Among Culturally Diverse Populations: Case Study From Finland.","authors":"Anna-Leena Lohiniva,&nbsp;Katja Sibenberg,&nbsp;Sara Austero,&nbsp;Natalia Skogberg","doi":"10.2196/38343","DOIUrl":"https://doi.org/10.2196/38343","url":null,"abstract":"<p><strong>Background: </strong>Social listening, the process of monitoring and analyzing conversations to inform communication activities, is an essential component of infodemic management. It helps inform context-specific communication strategies that are culturally acceptable and appropriate for various subpopulations. Social listening is based on the notion that target audiences themselves can best define their own information needs and messages.</p><p><strong>Objective: </strong>This study aimed to describe the development of systematic social listening training for crisis communication and community outreach during the COVID-19 pandemic through a series of web-based workshops and to report the experiences of the workshop participants implementing the projects.</p><p><strong>Methods: </strong>A multidisciplinary team of experts developed a series of web-based training sessions for individuals responsible for community outreach or communication among linguistically diverse populations. The participants had no previous training in systematic data collection or monitoring. This training aimed to provide participants with sufficient knowledge and skills to develop a social listening system based on their specific needs and available resources. The workshop design took into consideration the pandemic context and focused on qualitative data collection. Information on the experiences of the participants in the training was gathered based on participant feedback and their assignments and through in-depth interviews with each team.</p><p><strong>Results: </strong>A series of 6 web-based workshops was conducted between May and September 2021. The workshops followed a systematic approach to social listening and included listening to web-based and offline sources; rapid qualitative analysis and synthesis; and developing communication recommendations, messages, and products. Follow-up meetings were organized between the workshops during which participants could share their achievements and challenges. Approximately 67% (4/6) of the participating teams established social listening systems by the end of the training. The teams tailored the knowledge provided during the training to their specific needs. As a result, the social systems developed by the teams had slightly different structures, target audiences, and aims. All resulting social listening systems followed the taught key principles of systematic social listening to collect and analyze data and used these new insights for further development of communication strategies.</p><p><strong>Conclusions: </strong>This paper describes an infodemic management system and workflow based on qualitative inquiry and adapted to local priorities and resources. The implementation of these projects resulted in content development for targeted risk communication, addressing linguistically diverse populations. These systems can be adapted for future epidemics and pandemics.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e38343"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9421219","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":"Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.","authors":"Bente Christensen,&nbsp;Daniel Laydon,&nbsp;Tadeusz Chelkowski,&nbsp;Dariusz Jemielniak,&nbsp;Michaela Vollmer,&nbsp;Samir Bhatt,&nbsp;Konrad Krawczyk","doi":"10.2196/35121","DOIUrl":"https://doi.org/10.2196/35121","url":null,"abstract":"<p><strong>Background: </strong>Achieving herd immunity through vaccination depends upon the public's acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear communication of often complex information and, increasingly, the countering of misinformation. The primary outlet shaping public understanding is mainstream online news media, where coverage of COVID-19 vaccines was widespread.</p><p><strong>Objective: </strong>We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage.</p><p><strong>Methods: </strong>We analyzed 28 million articles from 172 major news sources across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of overall articles devoted to vaccines. We performed topic detection using BERTopic and named entity recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all collated English-language articles.</p><p><strong>Results: </strong>The proportion of front-page articles mentioning vaccines increased from 0.1% to 4% with the outbreak of COVID-19. The number of negatively polarized articles increased from 6698 in 2015-2019 to 28,552 in 2020-2021. However, overall vaccine coverage before the COVID-19 pandemic was slightly negatively polarized (57% negative), whereas coverage during the pandemic was positively polarized (38% negative).</p><p><strong>Conclusions: </strong>Throughout the pandemic, vaccines have risen from a marginal to a widely discussed topic on the front pages of major news outlets. Mainstream online media has been positively polarized toward vaccines, compared with mainly negative prepandemic vaccine news. However, the pandemic was accompanied by an order-of-magnitude increase in vaccine news that, due to low prepandemic frequency, may contribute to a perceived negative sentiment. These results highlight important interactions between the volume of news and overall polarization. To the best of our knowledge, our work is the first systematic text mining study of front-page vaccine news headlines in the context of COVID-19.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"2 2","pages":"e35121"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10793809","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}