Publication Date

Summer 8-1-2023

Abstract

In this dissertation, three primary issues are explored. The first subject exposes who-saw-from-whom pathways in post-specific dissemination networks in social media platforms. We describe a network-based approach for temporal, textual, and post-diffusion network inference. The conditional point process method discovers the most probable diffusion network. The tool is capable of meaningful analysis of hundreds of post shares. Inferred diffusion networks demonstrate disparities in information distribution between user groups (confirmed versus unverified, conservative versus liberal) and local communities (political, entrepreneurial, etc.). A promising approach for quantifying post-impact, we observe discrepancies in inferred networks that indicate the disproportionate amount of automated bots. Determine the most common organizational, political, and ideological dissemination pathways on Twitter. More misleading postings are followed by relatives and friends. The second theme is phylogenetics-related. In phylogenetics, likelihood techniques utilize a vast and diverse parameter space, which makes model selection more of a classification difficulty than an estimation one. We present a rooted triple approach for evolutionary tree inference that uses inter-taxon distances and k-fold cross-validation to assess if each triplet is tree-like. This new classification algorithm may be used to statistically infer level-1 networks. The final point pertains to temporal fairness. Customers in a service queue (such as a 311 call center) anticipate reasonable response times, particularly when there is no need to interrupt the first-come, first-served order. A temporally fair system delivers statistically equivalent service durations across sensitive population groups while permitting temporal fluctuations. We demonstrate that 311 service lines have treated demographic groups unevenly. We demonstrate that actual data are temporally unfair. Using our method, we slightly tweak the data in order to preserve statistical parity across groups and service times.

Degree Name

Statistics

Level of Degree

Doctoral

Department Name

Mathematics & Statistics

First Committee Member (Chair)

James Degnan

Second Committee Member

Abdullah Mueen

Third Committee Member

Ronald Christensen

Fourth Committee Member

Yan Lu

Language

English

Keywords

Social Media Networks, Phylogenetics, Fairness, Probabilistics models

Document Type

Dissertation

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