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• W3034949062 abstract "With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision of social sensing-based risk alert systems to spontaneously obtain and analyze social data to infer the state of the COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas by inferring future propagation patterns. The CovidSens concept is motivated by three observations: (1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, (2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media, and (3) online users are frequently equipped with substantially capable mobile devices that are able to perform non-trivial on-device computation for data processing and analytics. We envision an unprecedented opportunity to leverage the posts generated by the ordinary people to build a real-time sensing and analytic system for gathering and circulating vital information of the COVID-19 propagation. Specifically, the vision of CovidSens attempts to answer the questions: How to distill reliable information about the COVID-19 with the coexistence of prevailing rumors and misinformation in the social media? How to inform the general public about the latest state of the spread timely and effectively, and alert them to remain prepared? How to leverage the computational power on the edge devices (e.g., smartphones, IoT devices, UAVs) to construct fully integrated edge-based social sensing platforms for rapid detection of the COVID-19 spread? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in developing reliable social sensing-based risk alert systems. We envision that approaches originating from multiple disciplines (e.g., AI, estimation theory, machine learning, constrained optimization) can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens." @default.
• W3034949062 created "2020-06-19" @default.
• W3034949062 creator A5025156730 @default.
• W3034949062 creator A5046454314 @default.
• W3034949062 date "2020-06-12" @default.
• W3034949062 modified "2023-10-15" @default.
• W3034949062 title "CovidSens: a vision on reliable social sensing for COVID-19" @default.
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• W3034949062 doi "https://doi.org/10.1007/s10462-020-09852-3" @default.
• W3034949062 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7291936" @default.
• W3034949062 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32836651" @default.

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