Homophily and influence in online interactions

Abstract

May2025School of ScienceWith the volume of social interactions that occur online every day and the increasing relevance online social spaces have in information dissemination, it is important to understand how interactions between people and other actors online affect how people adopt opinions and consume information. Some of these behaviors, like homophily, echo chamber formation, and misinformation can result in different populations of social media users consuming entirely non-overlapping sets of information about events occurring around them. These behaviors can drive the polarization of people online and increase misunderstanding between groups of ideologically divided people. This thesis aims to examine these behaviors in social networks, particularly how people are influenced online, how this influence is being used online, how to detect these groups of individuals, and how to test / validate these methods. To identify and quantify these behaviors, this thesis performs two main analyses on social networks and human behavior. We first analyze social behaviors directly, by both developing social experiments, tested on human participants, to test human opinion dynamics directly and by analyzing human opinion dynamics in large Twitter datasets. In this Twitter dataset, we analyze millions of tweets, retweets and replies to examine information diffusion before the 2016 and 2020 U.S. presidential elections, identify the influencers that propagate information, and examine how these influencers and their interactions changed between the two periods. In this work we find that people online are susceptible to both the influence of unknown / anonymous users online as well as opinions / messages being propagated by bots or LLM agents. We also find that the information diffusion between influencers and users of Twitter have polarized further between the 2016 and 2020 elections, and that the set of influential accounts has begun to shy away from established media type accounts and shift to strong political personalities and unaffiliated lesser known people online. In second set of works, we both develop methods for detecting clusters of users in social networks and for generating networks that are adequate benchmarks for testing such methods. We build on existing Modularity community detection methods and extend them to handle issues that occur in large / heterogeneous networks. We then identify and explore the creation of synthetic benchmark networks, defining properties necessary to generate networks with heterogeneous inter-community connectivity, creating hierarchical community structure and better representing behaviors present in many real world social networks.Ph