Author(s)

FABIO GARZIA, FRANCESCO BORGHINI, JESSICA BOVE, MARA LOMBARDI, SOODAMANI RAMALINGAM

Abstract

Public safety, security and risk perception is an important aspect considered in opinion mining and sentiment analysis typically carried out on social networks. This involves considering each individual’s opinion and determining a sense of what the public feels about an incident, event or place. In that sense, social networks play an important role in capturing the emotions of people. Security and safety managers can employ opinion mining and sentiment analysis as a tool to discover any unforeseen vulnerabilities in a precise manner and thereby plan and manage any associated risks. Furthermore, a continuous evaluation of risk perception can be carried out for timely and planned interventions in a seamless, effective manner to reduce or avoid any panic amongst communities. Without such advance techniques, safety and security of people, infrastructures and specific contexts can be easily compromised. Recent work in this direction has shown promising results in managing risks, especially during the COVID-19 pandemic. The purpose of the present work is to investigate the perception of risk associated with different payment systems, in Italy and the UK, during the COVID-19 pandemic, from 10 November 2020 to 13 May 2021, by means of the semantic analysis of the textual contents existing in Twitter.

Keywords

perceived risk assessment for security, open-source intelligent techniques for security, OSINT, opinion mining for security, sentiment analysis for security, payment systems risk perception