In the last few years, thanks to the emergence of Web 2.0, social media has made the concept of online live events possible. Users participate more and more in long-running recurring events in social media by sharing their experiences and desires. In the last few years, thanks to the emergence of Web 2.0, social media has made the concept of online live events possible. Users participate more and more in long-running recurring events in social media by sharing their experiences and desires. This work introduces long-running live events (LRLEs), as a type of activity that span physical spaces and digital ecosystems, including social media. LRLEs encompass several individuals, organizations, and brands collaborating/competing in the same event. This provides unprecedented opportunities to understand the dynamics and behavior of event-oriented participation, through collection and analysis of data of user behaviors enabled by the Web platform, where most of the digital traces are left by users. What makes this setting interesting is that the behaviors that are traced are not focused only on one individual brand or organization, and thus allows one to understand and compare the respective roles and influence in a defined setting. In this paper we provide a high-level and multi-perspective roadmap to mine, model, and study LRLEs. Among the various aspects, we develop a multi-modal approach to solve the problem of post popularity prediction that exploits potentially influential factors within LRLE. We employ two methods for implementing feature selection, together with an automated grid search for optimizing hyper-parameters in various regression methods.

A multi-perspective approach for analyzing long-running live events on social media. A case study on the “Big Four” international fashion weeks

Javadian Sabet, Alireza;Brambilla, Marco;Hosseini, Marjan
2021-01-01

Abstract

In the last few years, thanks to the emergence of Web 2.0, social media has made the concept of online live events possible. Users participate more and more in long-running recurring events in social media by sharing their experiences and desires. In the last few years, thanks to the emergence of Web 2.0, social media has made the concept of online live events possible. Users participate more and more in long-running recurring events in social media by sharing their experiences and desires. This work introduces long-running live events (LRLEs), as a type of activity that span physical spaces and digital ecosystems, including social media. LRLEs encompass several individuals, organizations, and brands collaborating/competing in the same event. This provides unprecedented opportunities to understand the dynamics and behavior of event-oriented participation, through collection and analysis of data of user behaviors enabled by the Web platform, where most of the digital traces are left by users. What makes this setting interesting is that the behaviors that are traced are not focused only on one individual brand or organization, and thus allows one to understand and compare the respective roles and influence in a defined setting. In this paper we provide a high-level and multi-perspective roadmap to mine, model, and study LRLEs. Among the various aspects, we develop a multi-modal approach to solve the problem of post popularity prediction that exploits potentially influential factors within LRLE. We employ two methods for implementing feature selection, together with an automated grid search for optimizing hyper-parameters in various regression methods.
2021
Popularity prediction
Digital ecosystems
Long-running live event
Fashion week
Big data
Social media
Brand analysis
User generated content
Instagram
Feature selection
File in questo prodotto:
File Dimensione Formato  
LRLE_OSNEM.pdf

embargo fino al 10/06/2023

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 4.24 MB
Formato Adobe PDF
4.24 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1177001
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? ND
social impact