Graduation Year

2023

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Computer Science and Engineering

Major Professor

Sriram Chellappan, Ph.D.

Co-Major Professor

Adriana Iamnitchi, Ph.D.

Committee Member

John Skvoretz, Ph.D.

Committee Member

Giovanni L. Ciampaglia, Ph.D.

Committee Member

Michael Maness, Ph.D.

Keywords

Communities, Diffusion, Persistent minorities, Polarized Networks, Synthetic networks

Abstract

Social influence plays a significant role in shaping opinions and behaviors both online and inthe physical world. The ways in which we interact with others and the information we receive can significantly influence our beliefs and attitudes towards different issues. While online social media provide platforms for such interactions, the underlying network structure can impact various social influence phenomena, including majority illusion, behavior adoption, and polarization.

This dissertation examines the role of network topology in three social influence processes:majority illusion, behavior adoption, and polarization. The majority illusion refers to a perception bias that occurs when individuals believe that an opinion or belief is more prevalent in their social network than it actually is. This study investigates how different network topologies impact the magnitude of this phenomenon and its potential influence on the formation of opinions and beliefs. Empirical findings demonstrate that the small-world network topology is more resistant to the majority illusion when compared to other classes of random networks, while the forest-fire model and Barabasi-Albert model are more susceptible to the phenomenon. Furthermore, the analysis focuses on examining the potential for users who enhance the majority illusion to promote a new behavior or convention in a network.

This research also delves into behavior adoption within polarized networks, where individualstend to adopt behaviors aligned with their beliefs, resulting in the formation of opinion-based communities. The study examines the diffusion of a new convention within polarized networks, particularly highlighting the role of persistent minorities who advocate for alternative perspectives, challenge prevailing norms, and promote their minority beliefs. The research seeks to investigate the diffusion of a new convention in a polarized network and the conditions under which it can spread, especially in the presence of a persistent minority. Identifying and measuring polarization within networks with multiple communities is another challenge addressed in this dissertation. Specifically, the investigation focuses on whether URLs shared in a controversial topic reveal signs of polarization within the network.

For empirical evaluation, two novel datasets are introduced: the White-Helmets Twitterinteraction network and the VoterFraud2020 domain network. The former captures the interactions among users on Twitter regarding the dissemination of information related to the White Helmets, while the latter comprises web domains of URLs shared by users in tweets pertaining to claims of voter fraud regarding the US 2020 presidential election. Additionally, the dissertation addresses the limitations of existing polarization measures that assume the network consists of only two opposing communities. A new heterophily-based metric is proposed, which takes into account the presence of multiple ideological, antagonistic communities in a polarized network.

Overall, this dissertation investigates how network topology impacts social influence phenomenasuch as majority illusion, adoption of behavior, and polarization. The findings can contribute to the development of strategies aimed at promoting diverse opinions and mitigating the adverse effects of polarization in future research.

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