Structure vs. Language: Investigating the Multi-factors of Asymmetric Opinions on Online Social Interrelationship with a Case Study

TL;DR

This paper explores how interactive language and structural context in online social networks reveal individuals' asymmetric opinions on their relationships, extending analysis from single relationships to network structures.

Contribution

It introduces a multi-factor analysis combining language features and network structure to better understand subjective opinions in social relationships.

Findings

Language features correlate with structural network context.

Structural factors influence perceived relationship asymmetry.

Analysis on Enron dataset supports the multi-factor approach.

Abstract

Though current researches often study the properties of online social relationship from an objective view, we also need to understand individuals' subjective opinions on their interrelationships in social computing studies. Inspired by the theories from sociolinguistics, the latest work indicates that interactive language can reveal individuals' asymmetric opinions on their interrelationship. In this work, in order to explain the opinions' asymmetry on interrelationship with more latent factors, we extend the investigation from single relationship to the structural context in online social network. We analyze the correlation between interactive language features and the structural context of interrelationships. The structural context of vertex, edges and triangles in social network are considered. With statistical analysis on Enron email dataset, we find that individuals' opinions (measured by interactive language features) on their interrelationship are related to some of their important structural context in social network. This result can help us to understand and measure the individuals' opinions on their interrelationship with more intrinsic information.