Date of Award
Spring 6-1-2021
Document Type
Thesis (Undergraduate)
Department or Program
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.
Recommended Citation
Mandic, Mihovil, "A configurable social network for running IRB-approved experiments" (2021). Dartmouth College Undergraduate Theses. 213.
https://digitalcommons.dartmouth.edu/senior_theses/213
Included in
Databases and Information Systems Commons, Data Science Commons, Models and Methods Commons, Other Computer Sciences Commons, Science and Technology Studies Commons, Social Psychology and Interaction Commons, Software Engineering Commons