Bridging the Gap: Commonality and Differences between Online and Offline COVID-19 Data

TL;DR

This paper investigates the relationship between online social media and offline news regarding COVID-19, analyzing their shared and distinct topics over two years to understand misinformation spread and information trends.

Contribution

It introduces a novel approach to bridging online and offline COVID-19 news data using online matrix factorization to analyze their commonalities and differences over time.

Findings

Online and offline COVID-19 news share some topics but also have unique ones.

The analysis reveals how online and offline data are interconnected and follow certain trends.

The study provides insights into misinformation propagation related to COVID-19.

Abstract

With the onset of the COVID-19 pandemic, news outlets and social media have become central tools for disseminating and consuming information. Because of their ease of access, users seek COVID-19-related information from online social media (i.e., online news) and news outlets (i.e., offline news). Online and offline news are often connected, sharing common topics while each has unique, different topics. A gap between these two news sources can lead to misinformation propagation. For instance, according to the Guardian, most COVID-19 misinformation comes from users on social media. Without fact-checking social media news, misinformation can lead to health threats. In this paper, we focus on the novel problem of bridging the gap between online and offline data by monitoring their common and distinct topics generated over time. We employ Twitter (online) and local news (offline) data for a time span of two years. Using online matrix factorization, we analyze and study online and offline COVID-19-related data differences and commonalities. We design experiments to show how online and offline data are linked together and what trends they follow.