The social media platform Twitter was used to monitor corn and soybean diseases in the United States during 2016 and 2017 as part of a campaign to involve crop scouts, farmers, educators, and agricultural advisors in disease data sharing. The purpose was to explore the feasibility of providing farmers and crop consultants with an easily accessible, user-friendly, no-cost platform for sharing disease observations with rapid information transfer and early warning capabilities. Two Twitter accounts were created, @soydisease and @corndisease, as part of an accessible data collection method for later input into the Integrated Pest Information Platform for Extension and Education (iPiPE). Multiple methods were employed to create awareness and recruit users, which included writing articles for extension and popular farm news outlets and directly contacting potential agribusiness and extension stakeholders. From the creation of the accounts in February 2016 through September 2017, there were 738 followers and 8,668 profile visits for @soydisease; and 1,149 followers and 17,294 profile visits for @corndisease, with a variety of contributors including university extension, industry agronomists and service providers, students, a commodity group, and agricultural news. During the 2016 and 2017 growing seasons, use of the Twitter disease-monitoring campaign successfully helped track the movement of southern rust (caused by Puccinia polysora) of corn northward, allowing for advanced notice for scouting efforts. Although this is only an initial attempt, it shows that representatives from across a wide variety of agricultural sectors can contribute to a plant disease monitoring system using a common social media engine.
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http://dx.doi.org/10.1094/PDIS-11-17-1862-SR | DOI Listing |
JMIR Med Educ
January 2025
Digital Society Initiative, University of Zurich, Zurich, Switzerland.
Background: The increased use of digital data in health research demands interdisciplinary collaborations to address its methodological complexities and challenges. This often entails merging the linear deductive approach of health research with the explorative iterative approach of data science. However, there is a lack of structured teaching courses and guidance on how to effectively and constructively bridge different disciplines and research approaches.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Institute of Learning Sciences and Technologies, National Tsing Hua University, Hsinchu, Taiwan.
Background: Health misinformation undermines responses to health crises, with social media amplifying the issue. Although organizations work to correct misinformation, challenges persist due to reasons such as the difficulty of effectively sharing corrections and information being overwhelming. At the same time, social media offers valuable interactive data, enabling researchers to analyze user engagement with health misinformation corrections and refine content design strategies.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Centre for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Selangor, Malaysia.
Background: Cardiovascular disease (CVD) is a major global health issue, with approximately 70% of cases linked to modifiable risk factors. Digital health solutions offer potential for CVD prevention; yet, their effectiveness in covering the full range of prevention strategies is uncertain.
Objective: This study aimed to synthesize current literature on digital solutions for CVD prevention, identify the key components of effective digital interventions, and highlight critical research gaps to inform the development of sustainable strategies for CVD prevention.
Proc Natl Acad Sci U S A
January 2025
Department of Statistics and Data Science, College of Science, Southern University of Science and Technology, Shenzhen 518055, China.
Social media is profoundly changing our society with its unprecedented spreading power. Due to the complexity of human behaviors and the diversity of massive messages, the information-spreading dynamics are complicated, and the reported mechanisms are different and even controversial. Based on data from mainstream social media platforms, including WeChat, Weibo, and Twitter, cumulatively encompassing a total of 7.
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