Topical alignment in online social systems

TL;DR

This paper investigates whether users in online social media, specifically Twitter, tend to connect with others sharing similar interests by analyzing hashtags to model topical alignment, revealing that users are generally connected with similar others.

Contribution

It introduces a novel hashtag-based method to quantify topical interests and alignment among Twitter users, enhancing understanding of social network structure.

Findings

Users tend to connect with others sharing similar topics.

Topical alignment can predict user connectivity.

The method provides insights into social system organization.

Abstract

Understanding the dynamics of social interactions is crucial to comprehend human behavior. The emergence of online social media has enabled access to data regarding people relationships at a large scale. Twitter, specifically, is an information oriented network, with users sharing and consuming information. In this work, we study whether users tend to be in contact with people interested in similar topics, i.e., if they are topically aligned. To do so, we propose an approach based on the use of hashtags to extract information topics from Twitter messages and model users' interests. Our results show that, on average, users are connected with other users similar to them. Furthermore, we show that topical alignment provides interesting information that can eventually allow inferring users' connectivity. Our work, besides providing a way to assess the topical similarity of users, quantifies topical alignment among individuals, contributing to a better understanding of how complex social systems are structured.