TY - JOUR
T1 - How COVID-19 affects user interaction with online streaming service providers on twitter
AU - Arazzi, Marco
AU - Murer, Daniele
AU - Nicolazzo, Serena
AU - Nocera, Antonino
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - COVID-19
KW - Natural language processing
KW - Sentiment analysis
KW - Social network analysis
KW - Streaming service providers
KW - Twitter
UR - https://www.scopus.com/pages/publications/85174228706
U2 - 10.1007/s13278-023-01143-3
DO - 10.1007/s13278-023-01143-3
M3 - Article
SN - 1869-5450
VL - 13
JO - Social Network Analysis and Mining
JF - Social Network Analysis and Mining
IS - 1
M1 - 134
ER -