Date of Award

Spring 6-1-2021

Document Type

Thesis (Undergraduate)

Department

Department of Computer Science

First Advisor

Professor V.S. Subrahmanian

Second Advisor

Professor Soroush Vosoughi

Third Advisor

Professor Benjamin Valentino

Abstract

Our world has never been more connected, and the size of the social media landscape draws a great deal of attention from academia. However, social networks are also a growing challenge for the Institutional Review Boards concerned with the subjects’ privacy. These networks contain a monumental variety of personal information of almost 4 billion people, allow for precise social profiling, and serve as a primary news source for many users. They are perfect environments for influence operations that are becoming difficult to defend against. Motivated to study online social influence via IRB-approved experiments, we designed and implemented a flexible, scalable, and configurable social network called DartPost. DartPost allows us to run highly customized social network simulations and easily obtain subjects’ consent for collecting data typically exclusive to Big Tech engineers. This opens up a wide range of avenues for further research, including analysis of highly important impression data, often missing in other social influence studies.

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