514
Views
4
CrossRef citations to date
0
Altmetric
Articles

The role of network setting and gender in online content popularity

ORCID Icon, &
Pages 1607-1624 | Received 06 Jun 2016, Accepted 17 Oct 2016, Published online: 06 Nov 2016
 

ABSTRACT

In this study, we explore the role of specific network structures that enhance social capital and assess the extent to which gender, social ties, and communication interaction relate to content popularity within online social networks (OSNs). Our results are based on an extensive OSN data set, containing over 100,000 members, connected by over 1.7 million links. The findings indicate that content popularity inference is more accurate when considering activity interaction among users and that network structures known as advantageous for amassing social capital in the offline environment are relevant online as well. We conclude by discussing how gender mediates the correlation between some network measures and the growth of users’ content popularity and provide a potential explanation for the emergence of gender differences.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Ofrit Lesser is a lecturer at the Ben-Gurion University of the Negev, Israel. She is a high-tech veteran, and held several technical leading and management industry positions. Her research interests include Online Social Networks, Network Analysis, Data Mining, and Big Data. She holds B.Sc. and M.Sc. degrees in computer science from the Technion – Israel Institute of Technology and a PhD in Information Systems Engineering from the Ben-Gurion University. [email: [email protected]].

Dr Tsahi (Zack) Hayat is a faculty at the Sammy Ofer School of Communication, Interdisciplinary Center (IDC), Herzliya, Israel. His research focuses complex socio-technical systems, networks of people, artifacts, data, and ideas. He is particularly interested in how new technologies such as tablets, smartphones and social media platforms may enable or hinder the transfer of different resources within social networks. Dr Hayat's publications cover topics such as networked work, innovations, social support, and social network theory and methods. [email: [email protected]].

Yuval Elovici is the Director of the Telekom Innovation Laboratories at Ben-Gurion University of the Negev (BGU), Head of BGU Cyber Security Research Center, Research Director of iTrust at SUTD, Lab Director of ST Electronics – SUTD Cyber Security Laboratory, and a Professor in the Department of Information Systems Engineering at BGU. He holds BSc and MSc degrees in Computer and Electrical Engineering from BGU and a PhD in Information Systems from Tel-Aviv University. He served as the head of the software engineering program at BGU for two and a half years. For the past 12 years he has led the cooperation between BGU and Deutsche Telekom. Prof. Elovici has published articles in leading peer-reviewed journals and in various peer-reviewed conferences. In addition, he has co-authored a book on social network security and a book on information leakage detection and prevention. His primary research interests are computer and network security, cyber security, web intelligence, information warfare, social network analysis, and machine learning. Prof. Elovici also consults professionally in the area of cyber security and is the co-founder of Morphisec, a startup company that develop innovative cyber-security mechanisms that relate to moving target defense. [email: [email protected]].

Notes

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 304.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.