Dataset Information

An Open-Publishing Response to the COVID-19 Infodemic.

ABSTRACT: The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript's figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis.

SUBMITTER: Rando HM

PROVIDER: S-EPMC8452106 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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An Open-Publishing Response to the COVID-19 Infodemic.

Rando Halie M HM Boca Simina M SM McGowan Lucy D'Agostino LD Himmelstein Daniel S DS Robson Michael P MP Rubinetti Vincent V Velazquez Ryan R Greene Casey S CS Gitter Anthony A

ArXiv 20210917

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-1 ...[more]

PMID: 34545336

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Project description:BackgroundThe 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.ObjectiveThe aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response.MethodsWe 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.ResultsThe 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%.ConclusionsExternal 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 suggests that crowdsourced votes can optimize machine-learned labels to improve accuracy above human-only levels. These results support the utility of supervised machine learning to deter and combat future health-related disinformation.

Project description:BackgroundIn the context of the COVID-19 infodemic, the global profusion of monikers and hashtags for COVID-19 have found their way into daily communication and contributed to a backlash against China and the Chinese people.ObjectiveThis study examines public engagement in crisis communication about COVID-19 during the early epidemic stage and the practical strategy of social mobilization to mitigate the infodemic.MethodsWe retrieved the unbiased values of the top-ranked search phrases between December 30, 2019, and July 15, 2020, which normalized the anonymized, categorized, and aggregated samples from Google Search data. This study illustrates the most-searched terms, including the official COVID-19 terms, the stigmatized terms, and other controls, to measure the collective behavioral propensities to stigmatized terms and to explore the global reaction to the COVID-19 epidemic in the real world. We calculated the ratio of the cumulative number of COVID-19 cases to the regional population as the cumulative rate (R) of a specific country or territory and calculated the Gini coefficient (G) to measure the collective heterogeneity of crowd behavior.ResultsPeople around the world are using stigmatizing terms on Google Search, and these terms were used earlier than the official names. Many stigmatized monikers against China (eg, "Wuhan pneumonia," G=0.73; "Wuhan coronavirus," G=0.60; "China pneumonia," G=0.59; "China coronavirus," G=0.52; "Chinese coronavirus," G=0.50) had high collective heterogeneity of crowd behavior between December 30, 2019, and July 15, 2020, while the official terms "COVID-19" (G=0.44) and "SARS-CoV-2" (G=0.42) have not become de facto standard usages. Moreover, the pattern of high consistent usage was observed in 13 territories with low cumulative rates (R) between January 16 and July 15, 2020, out of 58 countries and territories that have reported confirmed cases of COVID-19. In the scientific literature, multifarious naming practices may have provoked unintended negative impacts by stigmatizing Chinese people. The World Health Organization; the United Nations Educational, Scientific and Cultural Organization; and the media initiated campaigns for fighting back against the COVID-19 infodemic with the same mission but in diverse voices.ConclusionsInfodemiological analysis can articulate the collective propensities to stigmatized monikers across search behaviors, which may reflect the collective sentiment of backlash against China and Chinese people in the real world. The full-fledged official terms are expected to fight back against the resilience of negative perceptual bias amid the COVID-19 epidemic. Such official naming efforts against the infodemic should be met with a fair share of identification in scientific conventions and sociocultural paradigms. As an integral component of preparedness, appropriate nomenclatures should be duly assigned to the newly identified coronavirus, and social mobilization in a uniform voice is a priority for combating the next infodemic.

Project description:BackgroundSince the beginning of the new coronavirus pandemic, there has been much information about the disease and the virus has been in the spotlight, shared and commented upon on the Internet. However, much of this information is infodemics and can interfere with the advancement of the disease and that way that populations act. Thus, Brazil is a country that requires attention, as despite the fact that in almost two years of pandemic it has shown a devastating numbers of deaths and number of cases, and generates false, distorted and malicious news about the pandemic. This work intends to understand the attitudes of the Brazilian population using infodemic queries from the Google Trends search tool and social and income variables.MethodsData from infodemic research carried out on Google Trends, between January 1, 2020 and June 30, 2021, with socioeconomic data, such as income and education, were unified in a single database: standardization and exploratory and multivalued techniques based on grouping were used in the study.ResultsIn the analysis of the search trend of infodemic terms, it is clear that the categories of Prevention and Beliefs should stand out in Brazil, where there is a diverse culture. It is followed by the COVID-19 Treatment category, with treatments that were not those recommended by the authorities. Income transfer programs and information on socioeconomic variables did not have much impact on infodemic surveys, but it was observed that states where President Bolsonaro has more supporters had researched more infodemic information.ConclusionsIn a country as geographically large as Brazil, it is important that political authorities go to great lengths to disseminate reliable information and monitor the infodemic in the media and on the internet. It was concluded that the denial of the pandemic and the influence of political leaders influenced the search for infodemic information, contributing to a disorganization in the control of the disease and prevention measures.

Project description:BackgroundThe COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people's health and governance systems.ObjectiveThis study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic.MethodsWe collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time.ResultsThe daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: "conspiracy theories" (648/2745, 23.61%), "government response" (544/2745, 19.82%), "prevention action" (411/2745, 14.97%), "new cases" (365/2745, 13.30%), "transmission routes" (244/2745, 8.89%), "origin and nomenclature" (228/2745, 8.30%), "vaccines and medicines" (154/2745, 5.61%), and "symptoms and detection" (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations.ConclusionsOur study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic.

Dataset Information

An Open-Publishing Response to the COVID-19 Infodemic.

Publications

An Open-Publishing Response to the COVID-19 Infodemic.

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