Uncovering the Structural Underpinnings of Political Discussion Networks
Evidence From an Exponential Random Graph Model
Bibliographic Data
| ID | 7539675 |
|---|---|
| Authors | Hyunjin Song (0000-0001-7752-3035, School of Communication; The Ohio State University; Columbus OH 43210 USA, corresponding author) |
| Year | 2014 |
| Volume | 65 |
| Issue | 1 |
| Pages | 146-169 |
| Publication date | 2014-12-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Communication (JOURNAL) |
| Journal identifiers | ISSN: 0021-9916 • E-ISSN: 1460-2466 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1111/jcom.12140 |
| OpenAlex | W1557027176 |
| Language | EN |
| Citations received | 19 |
| References cited | 79 |
How do individuals structure their political discussion networks, and what factors systematically shape such patterns? While much research has focused on the effect of personality traits and one's motivations, abilities, and opportunities, network-structural factors present different principles of tie formations. Evidence from an Exponential Family Random Graph Model and the meta-analysis of an Exponential Random Graph Model (Ergm) from 20 different groups' networks indicated that the pattern of political discussions was shaped by general discussion and network-endogenous structural processes. Results suggest that informal discussions of individuals serve as the important foundations of political behavior, and the processes of citizens' everyday political interactions emerge through complex interactions that cannot be regressed to mere individuals' predispositions or pure social selection processes
Exponential family · Exponential random graph models · Graph · Machine learning · Personality · Political science · Politics · Random graph · Sociology · Computer Science · Law · Opinion Dynamics and Social Influence · Psychology · Social Capital and Networks · Social Media and Politics · Social Psychology · Theoretical Computer Science
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| Unique citing works | 19 |
|---|---|
| Citations per year | 1,9 |
| Citation span | 2016 - 2025 (10) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 19 |