JMIR infodemiology最新文献

{"title":"Expert Credibility and Sentiment in Infodemiology of Hydroxychloroquine's Efficacy on Cable News Programs: Empirical Analysis.","authors":"Dobin Yim, Jiban Khuntia, Elliot King, Matthew Treskon, Panagis Galiatsatos","doi":"10.2196/45392","DOIUrl":"10.2196/45392","url":null,"abstract":"<p><strong>Background: </strong>Infodemic exacerbates public health concerns by disseminating unreliable and false scientific facts to a population. During the COVID-19 pandemic, the efficacy of hydroxychloroquine as a therapeutic solution emerged as a challenge to public health communication. Internet and social media spread information about hydroxychloroquine, whereas cable television was a vital source. To exemplify, experts discussed in cable television broadcasts about hydroxychloroquine for treating COVID-19. However, how the experts' comments influenced airtime allocation on cable television to help in public health communication, either during COVID-10 or at other times, is not understood.</p><p><strong>Objective: </strong>This study aimed to examine how 3 factors, that is, the credibility of experts as doctors (DOCTOREXPERT), the credibility of government representatives (GOVTEXPERT), and the sentiments (SENTIMENT) expressed in discussions and comments, influence the allocation of airtime (AIRTIME) in cable television broadcasts. SENTIMENT pertains to the information credibility conveyed through the tone and language of experts' comments during cable television broadcasts, in contrast to the individual credibility of the doctor or government representatives because of the degree or affiliations.</p><p><strong>Methods: </strong>We collected transcriptions of relevant hydroxychloroquine-related broadcasts on cable television between March 2020 and October 2020. We coded the experts as DOCTOREXPERT or GOVTEXPERT using publicly available data. To determine the sentiments expressed in the broadcasts, we used a machine learning algorithm to code them as POSITIVE, NEGATIVE, NEUTRAL, or MIXED sentiments.</p><p><strong>Results: </strong>The analysis revealed a counterintuitive association between the expertise of doctors (DOCTOREXPERT) and the allocation of airtime, with doctor experts receiving less airtime (P<.001) than the nonexperts in a base model. A more nuanced interaction model suggested that government experts with a doctorate degree received even less airtime (P=.03) compared with nonexperts. Sentiments expressed during the broadcasts played a significant role in airtime allocation, particularly for their direct effects on airtime allocation, more so for NEGATIVE (P<.001), NEUTRAL (P<.001), and MIXED (P=.03) sentiments. Only government experts expressing POSITIVE sentiments during the broadcast received a more extended airtime (P<.001) than nonexperts. Furthermore, NEGATIVE sentiments in the broadcasts were associated with less airtime both for DOCTOREXPERT (P<.001) and GOVTEXPERT (P<.001).</p><p><strong>Conclusions: </strong>Source credibility plays a crucial role in infodemics by ensuring the accuracy and trustworthiness of the information communicated to audiences. However, cable television media may prioritize likeability over credibility, potentially hindering this goal. Surprisingly, the findings of our study suggest that doctors","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9778440","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":"Content Quality of YouTube Videos About Pain Management After Cesarean Birth: Content Analysis.","authors":"Natalie A Squires,&nbsp;Elizabeth Soyemi,&nbsp;Lynn M Yee,&nbsp;Eleanor M Birch,&nbsp;Nevert Badreldin","doi":"10.2196/40802","DOIUrl":"https://doi.org/10.2196/40802","url":null,"abstract":"<p><strong>Background: </strong>YouTube is an increasingly common source of health information; however, the reliability and quality of the information are inadequately understood. Several studies have evaluated YouTube as a resource during pregnancy and found the available information to be of poor quality. Given the increasing attention to postpartum health and the importance of promoting safe opioid use after birth, YouTube may be a source of information for birthing individuals. However, little is known about the available information on YouTube regarding postpartum pain.</p><p><strong>Objective: </strong>The purpose of this study is to systematically evaluate the quality of YouTube videos as an educational resource for postpartum cesarean pain management.</p><p><strong>Methods: </strong>A systematic search of YouTube videos was conducted on June 25, 2021, using 36 postpartum cesarean pain management-related keywords, which were identified by clinical experts. The search replicated a default YouTube search via a public account. The first 60 results from each keyword search were reviewed, and unique videos were analyzed. An overall content score was developed based on prior literature and expert opinion to evaluate the video's relevance and comprehensiveness. The DISCERN instrument, a validated metric to assess consumer health information, was used to evaluate the reliability of video information. Videos with an overall content score of ≥5 and a DISCERN score of ≥39 were classified as high-quality health education resources. Descriptive analysis and intergroup comparisons by video source and quality were conducted.</p><p><strong>Results: </strong>Of 73 unique videos, video sources included medical videos (n=36, 49%), followed by personal video blogs (vlogs; n=32, 44%), advertisements (n=3, 4%), and media (n=2, 3%). The average overall content score was 3.6 (SD 2.0) out of 9, and the average DISCERN score was 39.2 (SD 8.1) out of 75, indicating low comprehensiveness and fair information reliability, respectively. High-quality videos (n=22, 30%) most frequently addressed overall content regarding pain duration (22/22, 100%), pain types (20/22, 91%), return-to-activity instructions (19/22, 86%), and nonpharmacologic methods for pain control (19/22, 86%). There were differences in the overall content score (P=.02) by video source but not DISCERN score (P=.45). Personal vlogs had the highest overall content score at 4.0 (SD 2.1), followed by medical videos at 3.3 (SD 2.0). Longer video duration and a greater number of comments and likes were significantly correlated with the overall content score, whereas the number of video comments was inversely correlated with the DISCERN score.</p><p><strong>Conclusions: </strong>Individuals seeking information from YouTube regarding postpartum cesarean pain management are likely to encounter videos that lack adequate comprehensiveness and reliability. Clinicians should counsel patients to exercise caution when","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10152372","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":"Establishing Infodemic Management in Germany: A Framework for Social Listening and Integrated Analysis to Report Infodemic Insights at the National Public Health Institute.","authors":"T Sonia Boender,&nbsp;Paula Helene Schneider,&nbsp;Claudia Houareau,&nbsp;Silvan Wehrli,&nbsp;Tina D Purnat,&nbsp;Atsuyoshi Ishizumi,&nbsp;Elisabeth Wilhelm,&nbsp;Christopher Voegeli,&nbsp;Lothar H Wieler,&nbsp;Christina Leuker","doi":"10.2196/43646","DOIUrl":"https://doi.org/10.2196/43646","url":null,"abstract":"<p><strong>Background: </strong>To respond to the need to establish infodemic management functions at the national public health institute in Germany (Robert Koch Institute, RKI), we explored and assessed available data sources, developed a social listening and integrated analysis framework, and defined when infodemic management functions should be activated during emergencies.</p><p><strong>Objective: </strong>We aimed to establish a framework for social listening and integrated analysis for public health in the German context using international examples and technical guidance documents for infodemic management.</p><p><strong>Methods: </strong>This study completed the following objectives: identified (potentially) available data sources for social listening and integrated analysis; assessed these data sources for their suitability and usefulness for integrated analysis in addition to an assessment of their risk using the RKI's standardized data protection requirements; developed a framework and workflow to combine social listening and integrated analysis to report back actionable infodemic insights for public health communications by the RKI and stakeholders; and defined criteria for activating integrated analysis structures in the context of a specific health event or health emergency.</p><p><strong>Results: </strong>We included and classified 38% (16/42) of the identified and assessed data sources for social listening and integrated analysis at the RKI into 3 categories: social media and web-based listening data, RKI-specific data, and infodemic insights. Most data sources can be analyzed weekly to detect current trends and narratives and to inform a timely response by reporting insights that include a risk assessment and scalar judgments of different narratives and themes.</p><p><strong>Conclusions: </strong>This study identified, assessed, and prioritized a wide range of data sources for social listening and integrated analysis to report actionable infodemic insights, ensuring a valuable first step in establishing and operationalizing infodemic management at the RKI. This case study also serves as a roadmap for others. Ultimately, once operational, these activities will inform better and targeted public health communication at the RKI and beyond.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10273031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10138898","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":"Advertising Alternative Cancer Treatments and Approaches on Meta Social Media Platforms: Content Analysis.","authors":"Marco Zenone,&nbsp;Jeremy Snyder,&nbsp;Jean-Christophe Bélisle-Pipon,&nbsp;Timothy Caulfield,&nbsp;May van Schalkwyk,&nbsp;Nason Maani","doi":"10.2196/43548","DOIUrl":"https://doi.org/10.2196/43548","url":null,"abstract":"<p><strong>Background: </strong>Alternative cancer treatment is associated with a greater risk of death than cancer patients undergoing conventional treatments. Anecdotal evidence suggests cancer patients view paid advertisements promoting alternative cancer treatment on social media, but the extent and nature of this advertising remain unknown. This context suggests an urgent need to investigate alternative cancer treatment advertising on social media.</p><p><strong>Objective: </strong>This study aimed to systematically analyze the advertising activities of prominent alternative cancer treatment practitioners on Meta platforms, including Facebook, Instagram, Messenger, and Audience Network. We specifically sought to determine (1) whether paid advertising for alternative cancer treatment occurs on Meta social media platforms, (2) the strategies and messages of alternative cancer providers to reach and appeal to prospective patients, and (3) how the efficacy of alternative treatments is portrayed.</p><p><strong>Methods: </strong>Between December 6, 2021, and December 12, 2021, we collected active advertisements from alternative cancer clinics using the Meta Ad Library. The information collected included identification number, URL, active/inactive status, dates launched/ran, advertiser page name, and a screenshot (image) or recording (video) of the advertisement. We then conducted a content analysis to determine how alternative cancer providers communicate the claimed benefits of their services and evaluated how they portrayed alternative cancer treatment efficacy.</p><p><strong>Results: </strong>We identified 310 paid advertisements from 11 alternative cancer clinics on Meta (Facebook, Instagram, or Messenger) marketing alternative treatment approaches, care, and interventions. Alternative cancer providers appealed to prospective patients through eight strategies: (1) advertiser representation as a legitimate medical provider (n=289, 93.2%); (2) appealing to persons with limited treatments options (n=203, 65.5%); (3) client testimonials (n=168, 54.2%); (4) promoting holistic approaches (n=121, 39%); (5) promoting messages of care (n=81, 26.1%); (6) rhetoric related to science and research (n=72, 23.2%); (7) rhetoric pertaining to the latest technology (n=63, 20.3%); and (8) focusing treatment on cancer origins and cause (n=43, 13.9%). Overall, 25.8% (n=80) of advertisements included a direct statement claiming provider treatment can cure cancer or prolong life.</p><p><strong>Conclusions: </strong>Our results provide evidence alternative cancer providers are using Meta advertising products to market scientifically unsupported cancer treatments. Advertisements regularly referenced \"alternative\" and \"natural\" treatment approaches to cancer. Imagery and text content that emulated evidence-based medical providers created the impression that the offered treatments were effective medical options for cancer. Advertisements exploited the hope of patients w","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9691446","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":"Exploring Chronic Pain and Pain Management Perspectives: Qualitative Pilot Analysis of Web-Based Health Community Posts.","authors":"Claire Harter,&nbsp;Marina Ness,&nbsp;Aleah Goldin,&nbsp;Christine Lee,&nbsp;Christine Merenda,&nbsp;Anne Riberdy,&nbsp;Anindita Saha,&nbsp;Richardae Araojo,&nbsp;Michelle Tarver","doi":"10.2196/41672","DOIUrl":"https://doi.org/10.2196/41672","url":null,"abstract":"<p><strong>Background: </strong>Patient perspectives are central to the US Food and Drug Administration's benefit-risk decision-making process in the evaluation of medical products. Traditional channels of communication may not be feasible for all patients and consumers. Social media websites have increasingly been recognized by researchers as a means to gain insights into patients' views about treatment and diagnostic options, the health care system, and their experiences living with their conditions. Consideration of multiple patient perspective data sources offers the Food and Drug Administration the opportunity to capture diverse patient voices and experiences with chronic pain.</p><p><strong>Objective: </strong>This pilot study explores posts from a web-based patient platform to gain insights into the key challenges and barriers to treatment faced by patients with chronic pain and their caregivers.</p><p><strong>Methods: </strong>This research compiles and analyzes unstructured patient data to draw out the key themes. To extract relevant posts for this study, predefined keywords were identified. Harvested posts were published between January 1, 2017, and October 22, 2019, and had to include #ChronicPain and at least one other relevant disease tag, a relevant chronic pain management tag, or a chronic pain management tag for a treatment or activity specific to chronic pain.</p><p><strong>Results: </strong>The most common topics discussed among persons living with chronic pain were related to disease burden, the need for support, advocacy, and proper diagnosis. Patients' discussions focused on the negative impact chronic pain had on their emotions, playing sports, or exercising, work and school, sleep, social life, and other activities of daily life. The 2 most frequently discussed treatments were opioids or narcotics and devices such as transcutaneous electrical nerve stimulation machines and spinal cord stimulators.</p><p><strong>Conclusions: </strong>Social listening data may provide valuable insights into patients' and caregivers' perspectives, preferences, and unmet needs, especially when conditions may be highly stigmatized.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10265428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9635548","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":"Global Misinformation Spillovers in the Vaccination Debate Before and During the COVID-19 Pandemic: Multilingual Twitter Study.","authors":"Jacopo Lenti,&nbsp;Yelena Mejova,&nbsp;Kyriaki Kalimeri,&nbsp;André Panisson,&nbsp;Daniela Paolotti,&nbsp;Michele Tizzani,&nbsp;Michele Starnini","doi":"10.2196/44714","DOIUrl":"https://doi.org/10.2196/44714","url":null,"abstract":"<p><strong>Background: </strong>Antivaccination views pervade online social media, fueling distrust in scientific expertise and increasing the number of vaccine-hesitant individuals. Although previous studies focused on specific countries, the COVID-19 pandemic has brought the vaccination discourse worldwide, underpinning the need to tackle low-credible information flows on a global scale to design effective countermeasures.</p><p><strong>Objective: </strong>This study aimed to quantify cross-border misinformation flows among users exposed to antivaccination (no-vax) content and the effects of content moderation on vaccine-related misinformation.</p><p><strong>Methods: </strong>We collected 316 million vaccine-related Twitter (Twitter, Inc) messages in 18 languages from October 2019 to March 2021. We geolocated users in 28 different countries and reconstructed a retweet network and cosharing network for each country. We identified communities of users exposed to no-vax content by detecting communities in the retweet network via hierarchical clustering and manual annotation. We collected a list of low-credibility domains and quantified the interactions and misinformation flows among no-vax communities of different countries.</p><p><strong>Results: </strong>The findings showed that during the pandemic, no-vax communities became more central in the country-specific debates and their cross-border connections strengthened, revealing a global Twitter antivaccination network. US users are central in this network, whereas Russian users also became net exporters of misinformation during vaccination rollout. Interestingly, we found that Twitter's content moderation efforts, in particular the suspension of users following the January 6 US Capitol attack, had a worldwide impact in reducing the spread of misinformation about vaccines.</p><p><strong>Conclusions: </strong>These findings may help public health institutions and social media platforms mitigate the spread of health-related, low-credibility information by revealing vulnerable web-based communities.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9915450","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":"Obesity-Related Discourse on Facebook and Instagram Throughout the COVID-19 Pandemic: Comparative Longitudinal Evaluation.","authors":"Catherine Pollack,&nbsp;Diane Gilbert-Diamond,&nbsp;Tracy Onega,&nbsp;Soroush Vosoughi,&nbsp;A James O'Malley,&nbsp;Jennifer A Emond","doi":"10.2196/40005","DOIUrl":"https://doi.org/10.2196/40005","url":null,"abstract":"<p><strong>Background: </strong>COVID-19 severity is amplified among individuals with obesity, which may have influenced mainstream media coverage of the disease by both improving understanding of the condition and increasing weight-related stigma.</p><p><strong>Objective: </strong>We aimed to measure obesity-related conversations on Facebook and Instagram around key dates during the first year of the COVID-19 pandemic.</p><p><strong>Methods: </strong>Public Facebook and Instagram posts were extracted for 29-day windows in 2020 around January 28 (the first US COVID-19 case), March 11 (when COVID-19 was declared a global pandemic), May 19 (when obesity and COVID-19 were linked in mainstream media), and October 2 (when former US president Trump contracted COVID-19 and obesity was mentioned most frequently in the mainstream media). Trends in daily posts and corresponding interactions were evaluated using interrupted time series. The 10 most frequent obesity-related topics on each platform were also examined.</p><p><strong>Results: </strong>On Facebook, there was a temporary increase in 2020 in obesity-related posts and interactions on May 19 (posts +405, 95% CI 166 to 645; interactions +294,930, 95% CI 125,986 to 463,874) and October 2 (posts +639, 95% CI 359 to 883; interactions +182,814, 95% CI 160,524 to 205,105). On Instagram, there were temporary increases in 2020 only in interactions on May 19 (+226,017, 95% CI 107,323 to 344,708) and October 2 (+156,974, 95% CI 89,757 to 224,192). Similar trends were not observed in controls. Five of the most frequent topics overlapped (COVID-19, bariatric surgery, weight loss stories, pediatric obesity, and sleep); additional topics specific to each platform included diet fads, food groups, and clickbait.</p><p><strong>Conclusions: </strong>Social media conversations surged in response to obesity-related public health news. Conversations contained both clinical and commercial content of possibly dubious accuracy. Our findings support the idea that major public health announcements may coincide with the spread of health-related content (truthful or otherwise) on social media.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10489454","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":"Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis.","authors":"Melissa C Eaton,&nbsp;Yasmine C Probst,&nbsp;Marc A Smith","doi":"10.2196/38245","DOIUrl":"https://doi.org/10.2196/38245","url":null,"abstract":"<p><strong>Background: </strong>Social media has transformed the way health messages are communicated. This has created new challenges and ethical considerations while providing a platform to share nutrition information for communities to connect and for information to spread. However, research exploring the web-based diet communities of popular diets is limited.</p><p><strong>Objective: </strong>This study aims to characterize the web-based discourse of popular diets, describe information dissemination, identify influential voices, and explore interactions between community networks and themes of mental health.</p><p><strong>Methods: </strong>This exploratory study used Twitter social media posts for an online social network analysis. Popular diet keywords were systematically developed, and data were collected and analyzed using the NodeXL metrics tool (Social Media Research Foundation) to determine the key network metrics (vertices, edges, cluster algorithms, graph visualization, centrality measures, text analysis, and time-series analytics).</p><p><strong>Results: </strong>The vegan and ketogenic diets had the largest networks, whereas the zone diet had the smallest network. In total, 31.2% (54/173) of the top users endorsed the corresponding diet, and 11% (19/173) claimed a health or science education, which included 1.2% (2/173) of dietitians. Complete fragmentation and hub and spoke messaging were the dominant network structures. In total, 69% (11/16) of the networks interacted, where the ketogenic diet was mentioned most, with depression and anxiety and eating disorder words most prominent in the \"zone diet\" network and the least prominent in the \"soy-free,\" \"vegan,\" \"dairy-free,\" and \"gluten-free\" diet networks.</p><p><strong>Conclusions: </strong>Social media activity reflects diet trends and provides a platform for nutrition information to spread through resharing. A longitudinal exploration of popular diet networks is needed to further understand the impact social media can have on dietary choices. Social media training is vital, and nutrition professionals must work together as a community to actively reshare evidence-based posts on the web.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9495787","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":"Estimating Rare Disease Incidences With Large-scale Internet Search Data: Development and Evaluation of a Two-step Machine Learning Method","authors":"Jiayu Li, Zhiyu He, M. Zhang, Weizhi Ma, Ye Jin, Lei Zhang, Shu-you Zhang, Yiqun Liu, Shaoping Ma","doi":"10.2196/42721","DOIUrl":"https://doi.org/10.2196/42721","url":null,"abstract":"Background As rare diseases (RDs) receive increasing attention, obtaining accurate RD incidence estimates has become an essential concern in public health. Since RDs are difficult to diagnose, include diverse types, and have scarce cases, traditional epidemiological methods are costly in RD registries. With the development of the internet, users have become accustomed to searching for disease-related information through search engines before seeking medical treatment. Therefore, online search data provide a new source for estimating RD incidences. Objective The aim of this study was to estimate the incidences of multiple RDs in distinct regions of China with online search data. Methods Our research scale included 15 RDs in China from 2016 to 2019. The online search data were obtained from Sogou, one of the top 3 commercial search engines in China. By matching to multilevel keywords related to 15 RDs during the 4 years, we retrieved keyword-matched RD-related queries. The queries used before and after the keyword-matched queries formed the basis of the RD-related search sessions. A two-step method was developed to estimate RD incidences with users’ intents conveyed by the sessions. In the first step, a combination of long short-term memory and multilayer perceptron algorithms was used to predict whether the intents of search sessions were RD-concerned, news-concerned, or others. The second step utilized a linear regression (LR) model to estimate the incidences of multiple RDs in distinct regions based on the RD- and news-concerned session numbers. For evaluation, the estimated incidences were compared with RD incidences collected from China’s national multicenter clinical database of RDs. The root mean square error (RMSE) and relative error rate (RER) were used as the evaluation metrics. Results The RD-related online data included 2,749,257 queries and 1,769,986 sessions from 1,380,186 users from 2016 to 2019. The best LR model with sessions as the input estimated the RD incidences with an RMSE of 0.017 (95% CI 0.016-0.017) and an RER of 0.365 (95% CI 0.341-0.388). The best LR model with queries as input had an RMSE of 0.023 (95% CI 0.017-0.029) and an RER of 0.511 (95% CI 0.377-0.645). Compared with queries, using session intents achieved an error decrease of 28.57% in terms of the RER (P=.01). Analysis of different RDs and regions showed that session input was more suitable for estimating the incidences of most diseases (14 of 15 RDs). Moreover, examples focusing on two RDs showed that news-concerned session intents reflected news of an outbreak and helped correct the overestimation of incidences. Experiments on RD types further indicated that type had no significant influence on the RD estimation task. Conclusions This work sheds light on a novel method for rapid estimation of RD incidences in the internet era, and demonstrates that search session intents were especially helpful for the estimation. The proposed two-step estimation method could ","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42972714","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":"Influence of User Profile Attributes on e-Cigarette-Related Searches on YouTube: Machine Learning Clustering and Classification.","authors":"Dhiraj Murthy, Juhan Lee, Hassan Dashtian, Grace Kong","doi":"10.2196/42218","DOIUrl":"10.2196/42218","url":null,"abstract":"<p><strong>Background: </strong>The proliferation of e-cigarette content on YouTube is concerning because of its possible effect on youth use behaviors. YouTube has a personalized search and recommendation algorithm that derives attributes from a user's profile, such as age and sex. However, little is known about whether e-cigarette content is shown differently based on user characteristics.</p><p><strong>Objective: </strong>The aim of this study was to understand the influence of age and sex attributes of user profiles on e-cigarette-related YouTube search results.</p><p><strong>Methods: </strong>We created 16 fictitious YouTube profiles with ages of 16 and 24 years, sex (female and male), and ethnicity/race to search for 18 e-cigarette-related search terms. We used unsupervised (k-means clustering and classification) and supervised (graph convolutional network) machine learning and network analysis to characterize the variation in the search results of each profile. We further examined whether user attributes may play a role in e-cigarette-related content exposure by using networks and degree centrality.</p><p><strong>Results: </strong>We analyzed 4201 nonduplicate videos. Our k-means clustering suggested that the videos could be clustered into 3 categories. The graph convolutional network achieved high accuracy (0.72). Videos were classified based on content into 4 categories: product review (49.3%), health information (15.1%), instruction (26.9%), and other (8.5%). Underage users were exposed mostly to instructional videos (37.5%), with some indication that more female 16-year-old profiles were exposed to this content, while young adult age groups (24 years) were exposed mostly to product review videos (39.2%).</p><p><strong>Conclusions: </strong>Our results indicate that demographic attributes factor into YouTube's algorithmic systems in the context of e-cigarette-related queries on YouTube. Specifically, differences in the age and sex attributes of user profiles do result in variance in both the videos presented in YouTube search results as well as in the types of these videos. We find that underage profiles were exposed to e-cigarette content despite YouTube's age-restriction policy that ostensibly prohibits certain e-cigarette content. Greater enforcement of policies to restrict youth access to e-cigarette content is needed.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10139687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9762510","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}