Identifying and assessing coordinated influence campaigns on social networks

• Main Author: Mesnards, Nicolas Guenon des.
• Other Authors: Tauhid Zaman.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2019
• Subjects: Operations Research Center.
• Online Access: https://hdl.handle.net/1721.1/122385

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 82-90). === Social networks have given us the ability to spread messages and influence large populations very easily. Malicious actors can take advantage of social networks to manipulate opinions using artificial accounts, or bots. It is suspected that the 2016 U.S. presidential election was the victim of such social network interference, potentially by foreign actors. Foreign influence bots are also suspected of having attacked European elections. The bots main action was the sharing of politically polarized content in an effort to shift opinions. In this work we present a method to identify coordinated influence campaigns, and quantify the impact of bots on the opinions of users in a social network. First, we provide evidence that modern bots in the social network Twitter coordinate their attacks. They do not create original content, but rather amplify certain human users by disproportionately retweeting them. We design a new algorithm for bot detection, and utilize the Ising model from statistical physics to model the network structure and bot labels. Then, we leverage a model for opinion dynamics in a social network, which we validate by showing that the user opinions predicted by the model align with the opinions of these users' based on their social media posts. Finally, we use the opinion model to calculate how the opinions shift when we remove the bots from the network. Our high level finding is that a small number of bots can have a disproportionate impact on the network opinions. === by Nicolas Guenon des Mesnards === S.M. === S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center