Committee Chair
Wang, Yingfeng
Committee Member
Liang, Yu; Asllani, Beni
College
College of Business
Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
Social media is a dynamic platform where a wide range of information is shared, including both true and false content, and it involves interactions between human users and social bots. This study investigates information diffusion patterns on X (formerly Twitter) by analyzing retweet (repost) network topologies. The results reveal distinct behavioral patterns for humans and bots when spreading true and false information, highlighting the need for further examination of their roles in information dissemination. Moreover, this study tackles the challenge of differentiating between broadcast and viral information diffusion on X, acknowledging the possibility of genuine information also being potentially misleading. Using observational data from retweet networks, a novel deterministic causal inference method is developed to classify diffusion types based on causality rather than structural virality. This innovative approach offers a valuable tool for assessing source credibility and aiding in identifying deceptive content. Importantly, there is potential for its extension to other social media platforms, offering a comprehensive strategy to comprehend and navigate information diffusion in the digital age.
Acknowledgments
I would like to express my heartfelt gratitude to my parents, Mehdi and Mahvash, for their unwavering support and encouragement throughout my academic journey. Your love and belief in me have been a constant source of motivation. I also want to extend my sincere appreciation to my supervisor, Dr. Yingfeng Wang, for his invaluable guidance and support, which was instrumental in the completion of this thesis.
Degree
M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.
Date
5-2024
Subject
Online manipulation; Online social networks; Selective dissemination of information; Social sciences--Network analysis; Twitterbots
Discipline
Business Analytics | Data Science
Document Type
Masters theses
DCMI Type
Text
Extent
xi, 51 leaves
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by/4.0/
Date Available
5-31-2025
Recommended Citation
Riazi, Amin, "Analyzing information diffusion in social media networks" (2024). Masters Theses and Doctoral Dissertations.
https://scholar.utc.edu/theses/856
Department
Dept. of Management