Disentangling Sources of Influence in Online Social Networks

Matija Piskorec,Tomislav Smuc,Mile Sikic

doi:10.1109/access.2019.2940762

Abstract

Information propagation in online social networks is facilitated by two types of influence - endogenous (peer) influence that acts between users of the social network and exogenous (external) that corresponds to various external mediators such as online news media. However, inference of these influences from data remains a challenge, especially when data on the activation of users is scarce. In this paper we propose a methodology that yields estimates of both endogenous and exogenous influence using only a social network structure and a single activation cascade. Our method exploits the statistical differences between the two types of influence - endogenous is dependent on the social network structure and current state of each user while exogenous is independent of these. We evaluate our methodology on simulated activation cascades as well as on cascades obtained from several large Facebook political survey applications. We show that our methodology is able to provide estimates of endogenous and exogenous influence in online social networks, characterize activation of each individual user as being endogenously or exogenously driven, and identify most influential groups of users.

Highlights

Popularity of online social networks allows us to investigate dynamics of social interactions on a scale that was previously unattainable [1]–[8], while at the same time raising ethical concerns not previously encountered [9], [10]
Mathematical modeling of social influence and information cascades is an active field of research in sociology for decades [11], [12], it only recently became technologically feasible to apply it to wide range of domains such as viral marketing [14], The associate editor coordinating the review of this manuscript and approving it for publication was Xiao Liu
Our log-likelihood would be t + 1-dimensional in the case of SI model, and t + 2-dimensional for the Exponential decay (EXP) model - t parameters of exogenous influence for each time window we are considering in our inference plus the parameters of endogenous influence (p0 for SI model and (p0, λ) for EXP model)

Summary

Introduction

Popularity of online social networks allows us to investigate dynamics of social interactions on a scale that was previously unattainable [1]–[8], while at the same time raising ethical concerns not previously encountered [9], [10]. Presence of exogenous factors is problematic in estimation of social influence as it confounds with the endogenous factors, and can be hard to differentiate using observational data alone [18] Still, it is instrumental for understanding the information spreading as information can propagate through multiple channels simultaneously, many of which are exogenous to the online social network itself - news media websites, direct communication via email and instant messengers, and even offline word-of-mouth transmission. External events such as political unrest [1], [19] and natural disasters [20] are often strong mediators of information cascades These exogenous influences are usually not directly observable in the online social network itself, they can be inferred from the available data. Understanding how endogenous and exogenous forces influence the information diffusion in online social networks could help us estimate to what extent are these vulnerable to manipulation

Methods

Results

Conclusion