Ego Network Structure in Online Social Networks and its Impact on Information Diffusion

TL;DR

This study analyzes ego network structures in online social networks like Facebook and Twitter, revealing their similarity to offline networks and demonstrating how tie strength and network layers influence information diffusion.

Contribution

It provides a detailed analysis of online ego networks, highlighting their structural similarities to offline networks and their role in information diffusion, especially in Twitter.

Findings

Online ego networks mirror offline social structures.

Tie strength correlates with retweet frequency.

Network layers influence information spread.

Abstract

In the last few years, Online Social Networks (OSNs) attracted the interest of a large number of researchers, thanks to their central role in the society. Through the analysis of OSNs, many social phenomena have been studied, such as the viral diffusion of information amongst people. What is still unclear is the relation between micro-level structural properties of OSNs (i.e. the properties of the personal networks of the users, also known as ego networks) and the emergence of such phenomena. A better knowledge of this relation could be essential for the creation of services for the Future Internet, such as highly personalised advertisements fitted on users' needs and characteristics. In this paper, we contribute to bridge this gap by analysing the ego networks of a large sample of Facebook and Twitter users. Our results indicate that micro-level structural properties of OSNs are interestingly similar to those found in social networks formed offline. In particular, online ego networks show the same structure found offline, with social contacts arranged in layers with compatible size and composition. From the analysis of Twitter ego networks, we have been able to find a direct impact of tie strength and ego network circles on the diffusion of information in the network. Specifically, there is a high correlation between the frequency of direct contact between users and her friends in Twitter (a proxy for tie strength), and the frequency of retweets made by the users from tweets generated by their friends. We analysed the correlation for each ego network layer identified in Twitter, discovering their role in the diffusion of information.