Analyzing information diffusion in social media networks

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.

Grantor dc:publisher

University of Tennessee at Chattanooga

Year dc:date.available

2025

Author dc:creator

• Riazi, Amin

Contributors dc:contributor

• Wang, Yingfeng
• Liang, Yu; Asllani, Beni
• College of Business

• Online manipulation
• Online social networks
• Selective dissemination of information
• Social sciences--Network analysis
• Twitterbots

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/856

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-2035