Computational Social Science of Social Cohesion and Polarization

This is an open access book. This edited volume explores the decay of social cohesion in democratic societies through the lens of Computational Social Science (CSS). Acknowledging its critical role in democratic stability, the UN and other organizations prioritize the strengthening of social cohesion. Despite these efforts, recent years have witnessed a decline in generalized trust, political polarization, and a loss of faith in democratic authorities. The book showcases that CSS, with its diverse tools and methodologies, is well-suited to address these challenges. Divided into two parts, the first section features contributions from senior scholars providing an introduction to social cohesion and polarization. It emphasizes the interconnected nature of these concepts and how CSS can push the discipline forward. Social cohesion is seen as a delicate balance between close-knit connections and positive societal connectivity, but also consensus on core values, norms and beliefs. This mirrors the study of political polarization, which traditionally focussed on attitudinal differences, and is nowadays much more about identity segregation and inter-party affection and connectivity. The book serves as a bridge between these topics, encouraging interdisciplinary exploration.

The second part showcases contributions from both senior and junior researchers applying CSS methods to study social cohesion and political polarization. Various approachestext-as-data analyses of parliamentary speeches and newspaper records, network science studies of opinion systems and co-sponsorship dynamics, and agent-based modeling exploring attitudes and identity-based influencehighlight the breadth of CSS tools. Together, they establish a foundation for CSS in understanding social cohesion and polarization, encouraging collaboration between traditional researchers in cohesion and polarization, and computational social scientists.

Provides cutting-edge interdisciplinary approaches Offers a methodological diversity and variation of empirical data sources Serves as a bridge between social cohesion and polarization This book is open access, which means that you have free and unlimited access

Autorentext

Marijn Keijzer is a research fellow at the Institute for Advanced Study in Toulouse and the Toulouse School of Economics. His research focuses on opinion dynamics and polarization, using a diverse set of methodologies from computational social science such as agent-based modeling, analysis of digital trace data and online (macro-)experiments. Marijn holds a PhD in Sociology (2022, ICS / University of Groningen).

Jan Lorenz is an assistant professor of social data science at Constructor University Bremen and faculty member at the Bremen International Graduate School of Social Sciences. He holds a Ph.D. in mathematics (2007, University Bremen) and a habilitation in computational social science at Constructr University. His research topics are models of opinion dynamics, social segregation, and other complex socio-economic systems. He did empirical research on the wisdom of crowds, measuring social cohesion, and polarization.

Micha Bojanowski is an assistant professor at the Chair of Quantitative Methods and Information Technology at Kozminski University and a post-doctoral researcher at the COALESCE Lab at the Autonomous University of Barcelona. He holds a PhD in sociology (2012, ICS / Utrecht University) and his research focuses on modeling social network data, especially collected with non-sociocentric designs as well as on assembling complex social network datasets from non-obvious sources (such as historical archives) often using technically-advanced procedures. Micha is an R developer with over 20 years of experience in writing packages and providing training in academic and business contexts. He is a member of Statnet Development Team -- the creators of a suite of R packages for statistical network analysis.

Inhalt

Computational Social Science of Social Cohesion and Polarization.- Social Cohesion and its Development in Germany before, during, and after COVID-19: The Bertelsmann Social Cohesion Radar.- Part I. Networks, Simulating an Empirically Informed Population Network of Core Discussion Ties.- The Anatomy of Rabbit Holes: Studying Information Segregation in YouTube's Recommendation Graph.- ResIN: A New Method to Analyze Socio-Political Attitude Systems.- Part II. Text-based methods, Elites and Polarisation: A Text and Sentiment Analytical Approach on the Dynamics of Polarized Political Discourses During Times of Crises.- Linguistic Polarization in Minority Representation: Analyzing Parliamentary Speeches in Germany and the UK (1980-2021).- Convergence in Framing: A Semantic Network Analysis of News Coverage of the LGBTQ Movement in the United States (1960s2010s).- Part III. Agent-based modelling, Understanding Mutual Social Influence When People Prefer Coherent Beliefs.- An Investigation into the Causal Mechanism of Political Opinion Dynamics: A Model of Hierarchical Coarse-Graining with Community-Bounded Social Influence.- Modeling Social Cohesion: The Influence of Memory and Learning.- Epilogue, From Summer Schools to Research Incubators in Computational Social Science and Beyond.