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Background: This article aims to analyze the relationship between user characteristics on social networks and influenza. Methods: Three specific research questions are investigated: (1) we classify Weibo updates to recognize influenza-related information based on machine learning algorithms and propose a quantitative model for influenza susceptibility in social networks; (2) we adopt in-degree indicator from complex networks theory as social media status to verify its coefficient correlation with influenza susceptibility; (3) we also apply the LDA topic model to explore users’ physical condition from Weibo to further calculate its coefficient correlation with influenza susceptibility. From the perspective of social networking status, we analyze and extract influenza-related information from social media, with many advantages including efficiency, low cost, and real time. Results: We find a moderate negative correlation between the susceptibility of users to influenza and social network status, while there is a significant positive correlation between physical condition and susceptibility to influenza. Conclusions: Our findings reveal the laws behind the phenomenon of online disease transmission, and providing important evidence for analyzing, predicting, and preventing disease transmission. Also, this study provides theoretical and methodological underpinnings for further exploration and measurement of more factors associated with infection control and public health from social networks.
Background: This article aims to analyze the relationship between user characteristics on social networks and influenza. Methods: Three specific research questions are investigated: (1) we classify Weibo updates to recognize influenza-related information based on machine learning algorithms and propose a quantitative model for influenza susceptibility in social networks; (2) we adopt in-degree indicator from complex networks theory as social media status to verify its coefficient correlation with influenza susceptibility; (3) we also apply the LDA topic model to explore users’ physical condition from Weibo to further calculate its coefficient correlation with influenza susceptibility. From the perspective of social networking status, we analyze and extract influenza-related information from social media, with many advantages including efficiency, low cost, and real time. Results: We find a moderate negative correlation between the susceptibility of users to influenza and social network status, while there is a significant positive correlation between physical condition and susceptibility to influenza. Conclusions: Our findings reveal the laws behind the phenomenon of online disease transmission, and providing important evidence for analyzing, predicting, and preventing disease transmission. Also, this study provides theoretical and methodological underpinnings for further exploration and measurement of more factors associated with infection control and public health from social networks.
ImportanceThe onset of the COVID-19 global pandemic highlighted the increasing role played by social media in the generation, dissemination and consumption of outbreak-related information.ObjectiveThe objective of the current review is to identify and summarise the role of social media in public health crises caused by infectious disease, using a five-step scoping review protocol.Evidence reviewKeyword lists for two categories were generated: social media and public health crisis. By combining these keywords, an advanced search of various relevant databases was performed to identify all articles of interest from 2000 to 2021, with an initial retrieval date of 13 December 2021. A total of six medical and health science, psychology, social science and communication databases were searched: PubMed, Web of Science, Scopus, Embase, PsycINFO and CNKI. A three-stage screening process against inclusion and exclusion criteria was conducted.FindingsA total of 338 studies were identified for data extraction, with the earliest study published in 2010. Thematic analysis of the role of social media revealed three broad themes: surveillance monitoring, risk communication and disease control. Within these themes, 12 subthemes were also identified. Within surveillance monitoring, the subthemes were disease detection and prediction, public attitude and attention, public sentiment and mental health. Within risk communication, the subthemes were health advice, information-seeking behaviour, infodemics/misinformation circulation, seeking help online, online distance education and telehealth. Finally, within disease control, the subthemes were government response, public behaviour change and health education information quality. It was clear that the pace of research in this area has gradually increased over time as social media has evolved, with an explosion in attention following the outbreak of COVID-19.Conclusions and relevanceSocial media has become a hugely powerful force in public health and cannot be ignored or viewed as a minor consideration when developing public health policy. Limitations of the study are discussed, along with implications for government, health authorities and individual users. The pressing need for government and health authorities to formalise evidence-based strategies for communicating via social media is highlighted, as well as issues for individual users in assessing the quality and reliability of information consumed on social media platforms.
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