11,970 research outputs found
Characterizing human collective behaviours of COVID-19 in Hong Kong
People are likely to engage in collective behaviour online during extreme
events, such as the COVID-19 crisis, to express their awareness, actions and
concerns. Hong Kong has implemented stringent public health and social measures
(PHSMs) to curb COVID-19 epidemic waves since the first COVID-19 case was
confirmed on 22 January 2020. People are likely to engage in collective
behaviour online during extreme events, such as the COVID-19 crisis, to express
their awareness, actions and concerns. Here, we offer a framework to evaluate
interactions among individuals emotions, perception, and online behaviours in
Hong Kong during the first two waves (February to June 2020) and found a strong
correlation between online behaviours of Google search and the real-time
reproduction numbers. To validate the model output of risk perception, we
conducted 10 rounds of cross-sectional telephone surveys from February 1
through June 20 in 2020 to quantify risk perception levels over time. Compared
with the survey results, the estimates of the risk perception of individuals
using our network-based mechanistic model capture 80% of the trend of people
risk perception (individuals who worried about being infected) during the
studied period. We may need to reinvigorate the public by engaging people as
part of the solution to live their lives with reduced risk
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
- …