How COVID-19 affects user interaction with online streaming service providers on twitter

The worldwide diffusion of COVID-19, declared pandemic in March 2020, has led to significant changes in people’s lifestyles and behavior, especially when it comes to the consumption of media and entertainment. Indeed, during this period, online streaming platforms have become the preferred providers...

Full description

Saved in:
Bibliographic Details
Published in:Social network analysis and mining 2023-10, Vol.13 (1), p.134
Main Authors: Arazzi, Marco, Murer, Daniele, Nicolazzo, Serena, Nocera, Antonino
Format: Article
Language:English
Subjects:
Connectedness
COVID-19
Data mining
Data science
Entertainment
Information management
Intelligence
Natural language processing
Pandemics
Sentiment analysis
Social networks
Streaming media
Technology adoption
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The worldwide diffusion of COVID-19, declared pandemic in March 2020, has led to significant changes in people’s lifestyles and behavior, especially when it comes to the consumption of media and entertainment. Indeed, during this period, online streaming platforms have become the preferred providers of recreational content, whereas Online Social Networks proved to be the favorite place to find social connections while adhering to distancing measures. In the meantime, from the online Streaming Service Providers’ point of view, Online Social Networks have gained more and more importance both as valuable data sources for business intelligence and as connected and co-viewing platforms. This study starts from these considerations to explore the impact of COVID-19 on user interaction with Streaming Service Providers in Online Social Networks. In particular, our investigation focuses on the Twitter platform; by comparing several large datasets referring to different periods (i.e., before, during, and after COVID-19 emergence), we investigate interesting patterns and dynamics leveraging both Natural Language Processing and sentiment analysis techniques. Our data science campaign, and the main findings derived, adopts a peculiar perspective focusing on the different categories of users and Streaming Service Providers. The main objective of the analysis is to uncover the dynamics underlying the evolution of the interaction between people and businesses during the COVID-19 outbreak.
ISSN:1869-5450
1869-5469
DOI:10.1007/s13278-023-01143-3