Networks in Political Communication, Advocacy, and Conflict
Open Access
- Author:
- Kim, Sang Yeon
- Graduate Program:
- Political Science
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- June 01, 2023
- Committee Members:
- Michael Nelson, Professor in Charge/Director of Graduate Studies
Cyanne Loyle, Major Field Member
Charles Seguin, Outside Unit & Field Member
Kevin Munger, Major Field Member
Bruce Desmarais, Chair & Dissertation Advisor - Keywords:
- Political Communication
Network Science
Political Science
Political Methodology
Social Media - Abstract:
- As most of political processes are interdependent in different ways by nature, studying embedded network structures of them is very important. None of the political actors are possible to act independently as their choices or actions affect other actors because of the connected nature of society. Therefore, to properly study political actors and processes, we have to know how the network structure of the political process we study looks like and how it conditions the process. Recognizing the political importance of networks, my dissertation studies how networks in different political events affect political outcomes focusing on dynamics in political communication, protest advocacy network, and intrastate conflicts. In the first chapter, I study patterns of political communication inside Twitch. I investigate a video-oriented social media platform that has seen little attention from social scientists: the entertainment-oriented live streaming platform Twitch. By using supervised machine learning methods, I have identified the political streamers and have collected the original data of chat posts from their live broadcasting. I conduct text analyses to observe what contents are covered in the political streams and network analyses to capture the interactions among political streamers and viewers of their streams. In the second chapter, I conduct an online experiment to explore how the political characteristics of accounts that share information affect how protest information is interpreted and diffused. In the third dissertation chapter, I propose a novel methodological approach to the dyadic spatial dependency of moving actors to improve our ability of conflict event forecasting, which also can help researchers to investigate how social media users’ dyadic interactions in online spaces are connected to their future movements in geographic space in the real world.