CovidSens: a vision on reliable social sensing for COVID-19

Abstract

With the spiraling pandemic of the Coronavirus Disease 2019 , 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.

IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution

“…Another important data analysis feature being used in tackling the spread of the corona-virus is trend analysis in combination with machine learning. COVIDSens [34] uses social media feeds and machine learning to track and trace virus propagation. Based on user experiences posted on social media, the application can filter useful new information for the benefit of the government and the general public.…”

“…New IoT technology to integrate social distancing in managing a global pandemic has been a major contributing factor to the evolution of IoT. We are now seeing state-of-the-art IoT systems using AI in combination with social media posts to warn communities to avoid high-risk areas [34]. Often when there is a breakout in a particular area, people tend to share reports and opinions on social media, mentioning locations and statistics in question.…”

“…In South Korea, the success of this mechanism is said to enable tracking of patient location within 10 minutes [53]. 5) Social media feeds: AI techniques such as COVIDSens [34] can extract posts and sentiments from social media as input data and provide a simulation of the COVID-19 virus spread. Using feeds from platforms like Twitter and Facebook, information on social distancing, virus hotspots and individual tracking can be extracted.…”

“…IoT big data and AI: Big data analytics and modeling can enable real-world simulation of the coronavirus spread. Several researchers are using artificial intelligence to better analyze COVID-19 big data and enable effective track and trace mechanisms[34],[46],[54]. Naude[55] mentions in an early review that AI is a powerful tool in the fight against COVID-19 and discussed several use-case scenarios for pandemic detection, tracking, tracing and spread prediction.…”

Reliability and trust perception of users on social media posts related to the ongoing COVID-19 pandemic

“…A new concept called CovidSens introduced to alert public about COVID-19 through timely, accurate information. It was caused because the official and the existing information systems were slow and so the people inclined to social media for news (Rashid & Wang, 2020).…”