Date of Award

3-2023

Document Type

Thesis

Degree Name

Master of Science in Operations Research

Department

Department of Operational Sciences

First Advisor

Phillip M. LaCasse, PhD

Abstract

This research trains, tests, and analyzes bot and troll classification models using publicly available, open source datasets. Specifically, it applies decision tree, random forest, feed forward neural networks, and long-short term memory neural networks with hyperparameters tuned via designed experiment to five labeled bot datasets created between 2011 and 2020 and one dataset labeling state-sponsored disinformation accounts or trolls. The first three models utilize account profile features, while the last model applies natural language processing techniques, specifically GloVe embedding, to analyze a user’s Tweet history. Results indicate that the random forest model outperforms the other three models with an average F1 score of approximately 0.879 for bot classification and 0.938 for troll classification.

AFIT Designator

AFIT-ENS-MS-23-M-143

Comments

A 12-month embargo was observed.

Approved for public release. PA case number on file.

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