Journal of Medical Internet Research

In Situation Report #3 and 39 days before declaring COVID-19 a pandemic, the WHO declared a -19 infodemic. The volume of coronavirus tweets was far too great for one to find accurate or reliable information. Healthcare workers were flooded with which drowned the of valuable COVID-19 information. To combat the infodemic, physicians created healthcare-specific micro-communities to share scientific information with other providers. We analyzed the content of eight physician-created communities and ...

BackgroundHealth agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) has been one of the leading agencies that utilizes social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between CDCs social media communication...

BackgroundPrevious research has shown that articles may be cited more frequently on the basis of title or abstract positivity. Whether a similar selective sharing practice exists on Twitter is not well understood. The objective of this study was to assess if COVID-19 articles with positive titles or abstracts were tweeted more frequently than those with non-positive titles or abstracts. MethodsCOVID-19 related articles published between January 1st and April 14th, 2020 were extracted from the L...

BackgroundThough infodemiological methods have been used in COVID-19 research, an examination of the extent of infodemic monikers (misinformation) use on the Internet remains limited. ObjectiveTo investigate Internet search behavior related to COVID-19 and examine the circulation of infodemic monikers through two platforms--Google and Instagram--during the current global pandemic. MethodsUsing Google Trends and Instagram hashtags (#), we explored Internet search activities and behaviors relat...

Palliative care is a specialized service with proven efficacy in improving patients quality-of-life. Nevertheless, lack of awareness and misunderstanding limits its adoption. Research is urgently needed to understand the determinants (e.g., knowledge) related to its adoption. Traditionally, these determinants are measured with questionnaires. In this study, we explored Twitter to reveal these determinants guided by the Integrated Behavioral Model. A secondary goal is to assess the feasibility of...

BackgroundTwitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the CDC activated its Emergency Operations Center and the WHO released its first situation report about Coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment has evolved in the early stages of any outbreak, including the COVID-19 epidemic, has not been described. ObjectiveTo quantify and understand early changes in...

Several people flocked to the Internet to learn about the SARS-CoV-2 and COVID-19 after the outbreak in Wuhan, China, in December 2019. As the novel coronavirus spread rapidly worldwide and was declared a global pandemic, the public rushed to Internet platforms to learn about the outbreak through Google search, online news outlets, and social media platforms. This paper evaluates the publics web search to learn about the pandemic and the possible impacts on attitude to the public health guidelin...

BackgroundSince the new coronavirus epidemic in China in December 2019, information and discussions about COVID-19 have spread rapidly on the Internet and have quickly become the focus of worldwide attention, especially on social media. ObjectiveThis study aims to investigate and analyze the publics attention to COVID-19-related events in China at the beginning of the COVID-19 epidemic in China (December 31, 2019, to February 20, 2020) through the Sina Microblog hot search list. MethodsWe coll...

BackgroundRespecting patient privacy and confidentiality is critical for doctor-patient relationships and public trust in medical professionals. The frequency of potentially identifiable disclosures online during periods of active engagement is unknown. Our aim was to quantify potentially identifiable content shared by physicians and other health care providers on social media using the hashtag #ShareAStoryInOneTweet. MethodsWe used Symplur Signals software to access Twitters API and searched f...

The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19.

BackgroundDuring disease outbreaks, social communication and behaviors are very important to contain the outbreak. Under such circumstances, individual activities on online platforms will increase tremendously. This will result in the circulation useful or misleading/misinformation (infodemic monikers) in the community. Thus, exploring the online trending information is highly crucial in the process of containing disease outbreak. Therefore, this study aimed to explore users concerns towards cor...

IntroductionWhen using social media, physicians are encouraged and trained to maintain separate professional and personal identities. However, this separation is difficult and even undesirable, as the blurring of personal and professional online presence can influence patient trust. Thus, to develop policies and educational resources that are more responsive to the blurring of personal and professional boundaries on social media, this study aims to provide an understanding of how physicians pres...

Social media have served as lucrative platforms for misinformation and for promoting fraudulent products for the treatment, testing and prevention of COVID-19. This has resulted in the issuance of many warning letters by the United States Food and Drug Administration (FDA). While social media continue to serve as the primary platform for the promotion of such fraudulent products, they also present the opportunity to identify these products early by employing effective social media mining methods...

IntroductioneHealth applications have been recognized as a valuable tool to reduce COVID-19s effective reproduction number. In this paper, we report on an online survey among Dutch citizens with the goal to identify antecedents of acceptance of a mobile application for COVID-19 symptom recognition and monitoring, and a mobile application for contact tracing. MethodsNext to the demographics, the online survey contained questions focussing on perceived health, fear of COVID-19 and intention to us...

BakcgroundYouTube is an important online source of information. And its viewing numbers tend to increase exponentially in extraordinary situations. Our aim in this study was to evaluate the contents of the most frequently viewed YouTube videos during the COVID-19 pandemic. MethodsIn this study, contents of the most frequently viewed Turkish and English videos regarding COVID-19 pandemics are examined and scored with modified DISCERN, MICI and VPI. ResultsThe mean DISCERN score of Turkish video...

The U.S. needs early warning systems to help it contain the spread of infectious diseases. Conventional early warning systems use lab-test results or dynamic records to signal early warning signs. New early warning systems can supplement these data with indicators of public awareness like news articles and search queries. This study aims to explore the potential of utilizing social media data to enhance early warning of the COVID-19 outbreak. To demonstrate the feasibility, this study conducts a...

In this paper, we introduce a machine-learning approach to detecting COVID-19-positive cases from self-reported information. Specifically, the proposed method builds a tree-based binary classification model that includes a recursive feature elimination step. Based on Shapley values, the recursive feature elimination method preserves the most relevant features without compromising the detection performance. In contrast to previous approaches that use a limited set of selected features, the machin...

ObjectiveThis study aimed to assess the quality, reliability and readability of internet-based information on COVID-19 available on Brazil most used search engines. MethodsA total of 68 websites were selected through Google, Bing, and Yahoo. The websites content quality and reliability were evaluated using the DISCERN questionnaire, the Journal of American Medical Association (JAMA) benchmark criteria, and the presence of the Health on Net (HON) certification. Readability was assessed by the Fl...

Telegram has emerged as a key platform for the circulation of conspiratorial narratives. We examine conspiratorial discourse within Singapore-based Telegram groups from 2021-2025. We analyze over 10 million words from three Telegram groups. We developed a logistic regression classifier to detect conspiratorial content, achieving an F1 score of 0.74 and expert-validated labeling accuracy of 72%. Topic models indicated dominant themes centered around elite control, vaccine risks, and globalist age...

Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the ...