JMIR infodemiology最新文献

{"title":"Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study.","authors":"Harshita Chopra,&nbsp;Aniket Vashishtha,&nbsp;Ridam Pal,&nbsp;Ananya Tyagi,&nbsp;Tavpritesh Sethi","doi":"10.2196/34315","DOIUrl":"https://doi.org/10.2196/34315","url":null,"abstract":"<p><strong>Background: </strong>Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online.</p><p><strong>Objective: </strong>This study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia.</p><p><strong>Methods: </strong>We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories-emotions and influencing factors. Using cosine distance from selected seed words' embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks.</p><p><strong>Results: </strong>Our findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41% to 39% in India. We also observed a significant change (<i>P</i><.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42% of tweets coming from India and 45% of tweets from the United States represented the \"vaccine_rollout\" category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases.</p><p><strong>Conclusions: </strong>By extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10165720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9540500","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":"Analyzing Discussions Around Rural Health on Twitter During the COVID-19 Pandemic: Social Network Analysis of Twitter Data.","authors":"Wasim Ahmed,&nbsp;Josep Vidal-Alaball,&nbsp;Josep Maria Vilaseca Llobet","doi":"10.2196/39209","DOIUrl":"https://doi.org/10.2196/39209","url":null,"abstract":"<p><strong>Background: </strong>Individuals from rural areas are increasingly using social media as a means of communication, receiving information, or actively complaining of inequalities and injustices.</p><p><strong>Objective: </strong>The aim of our study is to analyze conversations about rural health taking place on Twitter during a particular phase of the COVID-19 pandemic.</p><p><strong>Methods: </strong>This study captured 57 days' worth of Twitter data related to rural health from June to August 2021, using English-language keywords. The study used social network analysis and natural language processing to analyze the data.</p><p><strong>Results: </strong>It was found that Twitter served as a fruitful platform to raise awareness of problems faced by users living in rural areas. Overall, Twitter was used in rural areas to express complaints, debate, and share information.</p><p><strong>Conclusions: </strong>Twitter could be leveraged as a powerful social listening tool for individuals and organizations that want to gain insight into popular narratives around rural health.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10012181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9151991","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":"Potential Impact of the COVID-19 Pandemic on Public Perception of Water Pipes on Reddit: Observational Study.","authors":"Zihe Zheng,&nbsp;Zidian Xie,&nbsp;Maciej Goniewicz,&nbsp;Irfan Rahman,&nbsp;Dongmei Li","doi":"10.2196/40913","DOIUrl":"https://doi.org/10.2196/40913","url":null,"abstract":"<p><strong>Background: </strong>Socializing is one of the main motivations for water pipe smoking. Restrictions on social gatherings during the COVID-19 pandemic might have influenced water pipe smokers' behaviors. As one of the most popular social media platforms, Reddit has been used to study public opinions and user experiences.</p><p><strong>Objective: </strong>In this study, we aimed to examine the influence of the COVID-19 pandemic on public perception and discussion of water pipe tobacco smoking using Reddit data.</p><p><strong>Methods: </strong>We collected Reddit posts between December 1, 2018, and June 30, 2021, from a Reddit archive (PushShift) using keywords such as \"waterpipe,\" \"hookah,\" and \"shisha.\" We examined the temporal trend in Reddit posts mentioning water pipes and different locations (such as homes and lounges or bars). The temporal trend was further tested using interrupted time series analysis. Sentiment analysis was performed to study the change in sentiment of water pipe-related posts before and during the pandemic. Topic modeling using latent Dirichlet allocation (LDA) was used to examine major topics discussed in water pipe-related posts before and during the pandemic.</p><p><strong>Results: </strong>A total of 45,765 nonpromotion water pipe-related Reddit posts were collected and used for data analysis. We found that the weekly number of Reddit posts mentioning water pipes significantly increased at the beginning of the COVID-19 pandemic (<i>P</i><.001), and gradually decreased afterward (<i>P</i><.001). In contrast, Reddit posts mentioning water pipes and lounges or bars showed an opposite trend. Compared to the period before the COVID-19 pandemic, the average number of Reddit posts mentioning lounges or bars was lower at the beginning of the pandemic but gradually increased afterward, while the average number of Reddit posts mentioning the word \"home\" remained similar during the COVID-19 pandemic (<i>P</i>=.29). While water pipe-related posts with a positive sentiment were dominant (12,526/21,182, 59.14% before the pandemic; 14,686/24,583, 59.74% after the pandemic), there was no change in the proportion of water pipe-related posts with different sentiments before and during the pandemic (<i>P</i>=.19, <i>P</i>=.26, and <i>P</i>=.65 for positive, negative, and neutral posts, respectively). Most topics related to water pipes on Reddit were similar before and during the pandemic. There were more discussions about the opening and closing of hookah lounges or bars during the pandemic.</p><p><strong>Conclusions: </strong>This study provides a first evaluation of the possible impact of the COVID-19 pandemic on public perceptions of and discussions about water pipes on Reddit.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10126816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9762508","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 Messaging on Social Media for American Indian and Alaska Native Communities: Thematic Analysis of Audience Reach and Web Behavior.","authors":"Rose Weeks,&nbsp;Sydney White,&nbsp;Anna-Maria Hartner,&nbsp;Shea Littlepage,&nbsp;Jennifer Wolf,&nbsp;Kristin Masten,&nbsp;Lauren Tingey","doi":"10.2196/38441","DOIUrl":"https://doi.org/10.2196/38441","url":null,"abstract":"<p><strong>Background: </strong>During the COVID-19 pandemic, tribal and health organizations used social media to rapidly disseminate public health guidance highlighting protective behaviors such as masking and vaccination to mitigate the pandemic's disproportionate burden on American Indian and Alaska Native (AI/AN) communities.</p><p><strong>Objective: </strong>Seeking to provide guidance for future communication campaigns prioritizing AI/AN audiences, this study aimed to identify Twitter post characteristics associated with higher performance, measured by audience reach (impressions) and web behavior (engagement rate).</p><p><strong>Methods: </strong>We analyzed Twitter posts published by a campaign by the Johns Hopkins Center for Indigenous Health from July 2020 to June 2021. Qualitative analysis was informed by in-depth interviews with members of a Tribal Advisory Board and thematically organized according to the Health Belief Model. A general linearized model was used to analyze associations between Twitter post themes, impressions, and engagement rates.</p><p><strong>Results: </strong>The campaign published 162 Twitter messages, which organically generated 425,834 impressions and 6016 engagements. Iterative analysis of these Twitter posts identified 10 unique themes under theory- and culture-related categories of framing knowledge, cultural messaging, normalizing mitigation strategies, and interactive opportunities, which were corroborated by interviews with Tribal Advisory Board members. Statistical analysis of Twitter impressions and engagement rate by theme demonstrated that posts featuring culturally resonant community role models (<i>P</i>=.02), promoting web-based events (<i>P</i>=.002), and with messaging as part of Twitter Chats (<i>P</i><.001) were likely to generate higher impressions. In the adjusted analysis controlling for the date of posting, only the promotion of web-based events (<i>P</i>=.003) and Twitter Chat messaging (<i>P</i>=.01) remained significant. Visual, explanatory posts promoting self-efficacy (<i>P</i>=.01; <i>P</i>=.01) and humorous posts (<i>P</i>=.02; <i>P</i>=.01) were the most likely to generate high-engagement rates in both the adjusted and unadjusted analysis.</p><p><strong>Conclusions: </strong>Results from the 1-year Twitter campaign provide lessons to inform organizations designing social media messages to reach and engage AI/AN social media audiences. The use of interactive events, instructional graphics, and Indigenous humor are promising practices to engage community members, potentially opening audiences to receiving important and time-sensitive guidance.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9709694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35210823","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":"Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media.","authors":"Amélia Déguilhem,&nbsp;Joelle Malaab,&nbsp;Manissa Talmatkadi,&nbsp;Simon Renner,&nbsp;Pierre Foulquié,&nbsp;Guy Fagherazzi,&nbsp;Paul Loussikian,&nbsp;Tom Marty,&nbsp;Adel Mebarki,&nbsp;Nathalie Texier,&nbsp;Stephane Schuck","doi":"10.2196/39849","DOIUrl":"https://doi.org/10.2196/39849","url":null,"abstract":"<p><strong>Background: </strong>Long COVID-a condition with persistent symptoms post COVID-19 infection-is the first illness arising from social media. In France, the French hashtag #ApresJ20 described symptoms persisting longer than 20 days after contracting COVID-19. Faced with a lack of recognition from medical and official entities, patients formed communities on social media and described their symptoms as long-lasting, fluctuating, and multisystemic. While many studies on long COVID relied on traditional research methods with lengthy processes, social media offers a foundation for large-scale studies with a fast-flowing outburst of data.</p><p><strong>Objective: </strong>We aimed to identify and analyze Long Haulers' main reported symptoms, symptom co-occurrences, topics of discussion, difficulties encountered, and patient profiles.</p><p><strong>Methods: </strong>Data were extracted based on a list of pertinent keywords from public sites (eg, Twitter) and health-related forums (eg, Doctissimo). Reported symptoms were identified via the MedDRA dictionary, displayed per the volume of posts mentioning them, and aggregated at the user level. Associations were assessed by computing co-occurrences in users' messages, as pairs of preferred terms. Discussion topics were analyzed using the Biterm Topic Modeling; difficulties and unmet needs were explored manually. To identify patient profiles in relation to their symptoms, each preferred term's total was used to create user-level hierarchal clusters.</p><p><strong>Results: </strong>Between January 1, 2020, and August 10, 2021, overall, 15,364 messages were identified as originating from 6494 patients of long COVID or their caregivers. Our analyses revealed 3 major symptom co-occurrences: asthenia-dyspnea (102/289, 35.3%), asthenia-anxiety (65/289, 22.5%), and asthenia-headaches (50/289, 17.3%). The main reported difficulties were symptom management (150/424, 35.4% of messages), psychological impact (64/424,15.1%), significant pain (51/424, 12.0%), deterioration in general well-being (52/424, 12.3%), and impact on daily and professional life (40/424, 9.4% and 34/424, 8.0% of messages, respectively). We identified 3 profiles of patients in relation to their symptoms: profile A (n=406 patients) reported exclusively an asthenia symptom; profile B (n=129) expressed anxiety (n=129, 100%), asthenia (n=28, 21.7%), dyspnea (n=15, 11.6%), and ageusia (n=3, 2.3%); and profile C (n=141) described dyspnea (n=141, 100%), and asthenia (n=45, 31.9%). Approximately 49.1% of users (79/161) continued expressing symptoms after more than 3 months post infection, and 20.5% (33/161) after 1 year.</p><p><strong>Conclusions: </strong>Long COVID is a lingering condition that affects people worldwide, physically and psychologically. It impacts Long Haulers' quality of life, everyday tasks, and professional activities. Social media played an undeniable role in raising and delivering Long Haulers' voices and can potentially ra","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40710286","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-Related Health Inequalities Induced by the Use of Social Media: Systematic Review.","authors":"Yi Shan,&nbsp;Meng Ji,&nbsp;Wenxiu Xie,&nbsp;Xiaomin Zhang,&nbsp;Harrison Ng Chok,&nbsp;Rongying Li,&nbsp;Xiaobo Qian,&nbsp;Kam-Yiu Lam,&nbsp;Chi-Yin Chow,&nbsp;Tianyong Hao","doi":"10.2196/38453","DOIUrl":"https://doi.org/10.2196/38453","url":null,"abstract":"<p><strong>Background: </strong>COVID-19-related health inequalities were reported in some studies, showing the failure in public health and communication. Studies investigating the contexts and causes of these inequalities pointed to the contribution of communication inequality or poor health literacy and information access to engagement with health care services. However, no study exclusively dealt with health inequalities induced by the use of social media during COVID-19.</p><p><strong>Objective: </strong>This review aimed to identify and summarize COVID-19-related health inequalities induced by the use of social media and the associated contributing factors and to characterize the relationship between the use of social media and health disparities during the COVID-19 pandemic.</p><p><strong>Methods: </strong>A systematic review was conducted on this topic in light of the protocol of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement. Keyword searches were performed to collect papers relevant to this topic in multiple databases: PubMed (which includes MEDLINE [Ovid] and other subdatabases), ProQuest (which includes APA PsycINFO, Biological Science Collection, and others), ACM Digital Library, and Web of Science, without any year restriction. Of the 670 retrieved publications, 10 were initially selected based on the predefined selection criteria. These 10 articles were then subjected to quality analysis before being analyzed in the final synthesis and discussion.</p><p><strong>Results: </strong>Of the 10 articles, 1 was further removed for not meeting the quality assessment criteria. Finally, 9 articles were found to be eligible and selected for this review. We derived the characteristics of these studies in terms of publication years, journals, study locations, locations of study participants, study design, sample size, participant characteristics, and potential risk of bias, and the main results of these studies in terms of the types of social media, social media use-induced health inequalities, associated factors, and proposed resolutions. On the basis of the thematic synthesis of these extracted data, we derived 4 analytic themes, namely health information inaccessibility-induced health inequalities and proposed resolutions, misinformation-induced health inequalities and proposed resolutions, disproportionate attention to COVID-19 information and proposed resolutions, and higher odds of social media-induced psychological distress and proposed resolutions.</p><p><strong>Conclusions: </strong>This paper was the first systematic review on this topic. Our findings highlighted the great value of studying the COVID-19-related health knowledge gap, the digital technology-induced unequal distribution of health information, and the resulting health inequalities, thereby providing empirical evidence for understanding the relationship between social media use and health inequalities in the context of COVID-","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40702896","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 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach.","authors":"Si Yang Ke,&nbsp;E Shannon Neeley-Tass,&nbsp;Michael Barnes,&nbsp;Carl L Hanson,&nbsp;Christophe Giraud-Carrier,&nbsp;Quinn Snell","doi":"10.2196/37861","DOIUrl":"https://doi.org/10.2196/37861","url":null,"abstract":"<p><strong>Background: </strong>Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19-related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19.</p><p><strong>Objective: </strong>The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action.</p><p><strong>Methods: </strong>A total of 646,885,238 COVID-19-related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets.</p><p><strong>Results: </strong>In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action.</p><p><strong>Conclusions: </strong>During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40454692","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":"Investigating COVID-19 Vaccine Communication and Misinformation on TikTok: Cross-sectional Study.","authors":"Katherine van Kampen,&nbsp;Jeremi Laski,&nbsp;Gabrielle Herman,&nbsp;Teresa M Chan","doi":"10.2196/38316","DOIUrl":"https://doi.org/10.2196/38316","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has highlighted the need for reliable information, especially around vaccines. Vaccine hesitancy is a growing concern and a great threat to broader public health. The prevalence of social media within our daily lives emphasizes the importance of accurately analyzing how health information is being disseminated to the public. TikTok is of particular interest, as it is an emerging social media platform that young adults may be increasingly using to access health information.</p><p><strong>Objective: </strong>The objective of this study was to examine and describe the content within the top 100 TikToks trending with the hashtag #covidvaccine.</p><p><strong>Methods: </strong>The top 250 most viewed TikToks with the hashtag #covidvaccine were batch downloaded on July 1, 2021, with their respective metadata. Each TikTok was subsequently viewed and encoded by 2 independent reviewers. Coding continued until 100 TikToks could be included based on language and content. Descriptive features were recorded including health care professional (HCP) status of creator, verification of HCP status, genre, and misinformation addressed. Primary inclusion criteria were any TikToks in English with discussion of a COVID-19 vaccine.</p><p><strong>Results: </strong>Of 102 videos included, the median number of plays was 1,700,000, with median shares of 9224 and 62,200 followers. Upon analysis, 14.7% (15/102) of TikToks included HCPs, of which 80% (12/102) could be verified via social media or regulatory body search; 100% (15/15) of HCP-created TikToks supported vaccine use, and overall, 81.3% (83/102) of all TikToks (created by either a layperson or an HCP) supported vaccine use.</p><p><strong>Conclusions: </strong>As the pandemic continues, vaccine hesitancy poses a threat to lifting restrictions, and discovering reasons for this hesitancy is important to public health measures. This study summarizes the discourse around vaccine use on TikTok. Importantly, it opens a frank discussion about the necessity to incorporate new social media platforms into medical education, so we might ensure our trainees are ready to engage with patients on novel platforms.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40469448","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":"Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia.","authors":"Mengke Hu,&nbsp;Mike Conway","doi":"10.2196/36941","DOIUrl":"https://doi.org/10.2196/36941","url":null,"abstract":"<p><strong>Background: </strong>Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging.</p><p><strong>Objective: </strong>The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit.</p><p><strong>Methods: </strong>We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19-related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country.</p><p><strong>Results: </strong>Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19-related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits.</p><p><strong>Conclusions: </strong>This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9521381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33487377","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 Quality, Readability, and Accuracy of the Information on Google About Cannabis and Driving: Quantitative Content Analysis","authors":"Maria Josey, Dina Gaid, Lisa D. Bishop, Michael Blackwood, M. Najafizada, Jennifer R. Donnan","doi":"10.2196/43001","DOIUrl":"https://doi.org/10.2196/43001","url":null,"abstract":"Background The public perception of driving under the influence of cannabis (DUIC) is not consistent with current evidence. The internet is an influential source of information available for people to find information about cannabis. Objective The purpose of this study was to assess the quality, readability, and accuracy of the information about DUIC found on the internet using the Google Canada search engine. Methods A quantitative content analysis of the top Google search web pages was conducted to analyze the information available to the public about DUIC. Google searches were performed using keywords, and the first 20 pages were selected. Web pages or web-based resources were eligible if they had text on cannabis and driving in English. We assessed (1) the quality of information using the Quality Evaluation Scoring Tool (QUEST) and the presence of the Health on the Net (HON) code; (2) the readability of information using the Gunning Fox Index (GFI), Flesch Reading Ease Scale (FRES), Flesch-Kincaid Grade Level (FKGL), and Simple Measure of Gobbledygook (SMOG) scores; and (3) the accuracy of information pertaining to the effects of cannabis consumption, prevalence of DUIC, DUIC effects on driving ability, risk of collision, and detection by law enforcement using an adapted version of the 5Cs website evaluation tool. Results A total of 82 web pages were included in the data analysis. The average QUEST score was 17.4 (SD 5.6) out of 28. The average readability scores were 9.7 (SD 2.3) for FKGL, 11.4 (SD 2.9) for GFI, 12.2 (SD 1.9) for SMOG index, and 49.9 (SD 12.3) for FRES. The readability scores demonstrated that 8 (9.8%) to 16 (19.5%) web pages were considered readable by the public. The accuracy results showed that of the web pages that presented information on each key topic, 96% (22/23) of them were accurate about the effects of cannabis consumption; 97% (30/31) were accurate about the prevalence of DUIC; 92% (49/53) were accurate about the DUIC effects on driving ability; 80% (41/51) were accurate about the risk of collision; and 71% (35/49) were accurate about detection by law enforcement. Conclusions Health organizations should consider health literacy of the public when creating content to help prevent misinterpretation and perpetuate prevailing misperceptions surrounding DUIC. Delivering high quality, readable, and accurate information in a way that is comprehensible to the public is needed to support informed decision-making.","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42778663","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}