An Infoveillance System for Detecting and Tracking Relevant Topics From Italian Tweets During the COVID-19 Event.

Enrico De Santis,Alessio Martino,Antonello Rizzi

doi:10.1109/access.2020.3010033

Enrico De Santis, Alessio Martino + Show 1 more

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https://doi.org/10.1109/access.2020.3010033

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Abstract

The year 2020 opened with a dramatic epidemic caused by a new species of coronavirus that soon has been declared a pandemic by the WHO due to the high number of deaths and the critical mass of worldwide hospitalized patients, of order of millions. The COVID-19 pandemic has forced the governments of hundreds of countries to apply several heavy restrictions in the citizens’ socio-economic life. Italy was one of the most affected countries with long-term restrictions, impacting the socio-economic tissue. During this lockdown period, people got informed mostly on Online Social Media, where a heated debate followed all main ongoing events. In this scenario, the following study presents an in-depth analysis of the main emergent topics discussed during the lockdown phase within the Italian Twitter community. The analysis has been conducted through a general purpose methodological framework, grounded on a biological metaphor and on a chain of NLP and graph analysis techniques, in charge of detecting and tracking emerging topics in Online Social Media, e.g. streams of Twitter data. A term-frequency analysis in subsequent time slots is pipelined with nutrition and energy metrics for computing hot terms by also exploiting the tweets quality information, such as the social influence of the users. Finally, a co-occurrence analysis is adopted for building a topic graph where emerging topics are suitably selected. We demonstrate via a careful parameter setting the effectiveness of the topic tracking system, tailored to the current Twitter standard API restrictions, in capturing the main sociopolitical events that occurred during this dramatic phase.

Highlights

It is well established that Internet and, in particular Online Social Media (OSM), are an invaluable source of fresh information
In this work we proposed an in-depth analysis of the general debate within the Italian Twitter community during the lockdown period established in Italy for security reasons due to the dramatic COVID-19 pandemic
The methodology served as a driver to develop a topic tracking system tailored to modern Twitter standards and to the aim of retrieving buzzing terms and topics in the Italian language

Summary

Introduction

It is well established that Internet and, in particular Online Social Media (OSM), are an invaluable source of fresh information. Since 2006, the American online microblogging platform and social network service Twitter has gained rapidly more and more worldwide popularity with 321M active users in 2019. Considering the mass of active users and how they interact with the platform – many of them can be considered as sensors or amplifier of facts or happening events – the Twitter data stream possess an invaluable strength in the task of discovering and tracking real-world events. A vast literature shows how the Twitter data stream can be used for discovering, tracking and analyzing these real-world events, such as earthquakes and natural disasters [2]–[4] in earth science, or national security events such as terrorists attacks [5]–[7]. Twitter data have been widely used even for tracking and analyzing important sociopolitical events, such as the riots during the Arab Spring [8] and the process of opinion formation around major political themes [9]–[12], with particular attention to disinformation spreading [13]

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