An organizational form framework to measure and interpret online polarization

ABSTRACT

As social media becomes more ingrained in political and social discourse around the world, the need for systematic measures and interpretations of online polarization grows. In this work, we introduce five major organizational forms of community-level polarization and nine subforms to support comparison of community dynamics in controversial discussions across time, platforms, languages, and domains. We propose a multi-dimensional social network analysis approach to characterize the structure within and between ideologically opposed communities and evaluate the organizational form. Through three case studies, we demonstrate applying the proposed methodology on two social media platforms (Twitter and Reddit) and levels of conflict (topic and partisanship). This work has implications for depolarization interventions and policies on social media.

KEYWORDS:

• Polarization
• organizational studies
• social network analysis
• social media

Acknowledgments

The authors would like to express gratitude to Daniele Bellutta for notes on the original manuscipt.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing not applicable – no new data generated. Scripts to apply methodology will be made publicly available.

Notes

1 https://www.pewresearch.org/politics/2019/12/17/in-a-politically-polarized-era-sharp-divides-in-both-partisan-coalitions/.

2 https://twitter.com/demvoice1.

Additional information

Funding

The research for this paper was supported in part by the Knight Foundation [grant number G-2019-58792] and the Office of Naval Research under the following projects MURI:FACTIONS:Near Real Time Assessment of Emergent Complex Systems of Confederates [grant number N000141712675]; Minerva-Multi-Level Models of Covert Online Information Campaigns [grant number N000142112765]; Group Polarization in Social Media [grant number N000141812106]. This paper is also supported by the center for Informed Democracy and Social-cybersecurity (IDeaS) and the center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon University.

Notes on contributors

Samantha C. Phillips

Samantha C. Phillips is a PhD student in Societal Computing at Carnegie Mellon University. She is a graduate researcher at the Center Computational Analysis of Social and Organizational Systems (CASOS) and Center for Informed Democracy and Social Cybersecurity (IDeaS), studying the relationship between social media and influence operations, polarization, and extremism. [Email: [email protected]]

Kathleen M. Carley

Kathleen M. Carley is a Professor of Societal Computing, Software and Societal Systems, Carnegie Mellon University; Director of the Center for Computational Analysis of Social and Organizational Systems (CASOS), Director of the Center for Informed Democracy and Social Cybersecurity (IDeaS), and CEO of Netanomics. Her research blends computer science and social science to address complex real world issues such as social cybersecurity, disinformation, and terrorism from a high dimensional network analytic, machine learning, and natural language processing perspective. [Email: [email protected]]