Modeling peer and external influence in online social networks

TL;DR

This paper introduces a new method to estimate peer and external influence on voting behavior in online social networks, validated on real and simulated data, highlighting the impact of external events and peer communities.

Contribution

The paper presents a novel approach to infer peer and external influences from friendship networks and voting times, addressing a gap in understanding social influence dynamics.

Findings

External news articles correlate with voting peaks

Strongly connected communities tend to vote in short periods

Users tend to friend others with similar voting behavior

Abstract

Opinion polls mediated through a social network can give us, in addition to usual demographics data like age, gender and geographic location, a friendship structure between voters and the temporal dynamics of their activity during the voting process. Using a Facebook application we collected friendship relationships, demographics and votes of over ten thousand users on the referendum on the definition of marriage in Croatia held on 1st of December 2013. We also collected data on online news articles mentioning our application. Publication of these articles align closely with large peaks of voting activity, indicating that these external events have a crucial influence in engaging the voters. Also, existence of strongly connected friendship communities where majority of users vote during short time period, and the fact that majority of users in general tend to friend users that voted the same suggest that peer influence also has its role in engaging the voters. As we are not able to track activity of our users at all times, and we do not know their motivations for expressing their votes through our application, the question is whether we can infer peer and external influence using friendship network of users and the times of their voting. We propose a new method for estimation of magnitude of peer and external influence in friendship network and demonstrate its validity on both simulated and actual data.