Pathways Between Social Science and Computational Social Science: Theories, Methods, and Interpretations (Hardcover, 2021)

INTRODUCTION

The impact of Computational Social Science on the Social Sciences:

PART A: THEORY - DILEMMAS OF MODEL BUILDING AND INTERPRETATION

From Big Data to some deep thoughts - and back

Data driven modeling of complex networks of social interactions: Insight from ten years of explorative research

Formal design methods and the relation between simulation models and theory: A philosophy of science point of view

The social construction of knowledge in networks: Model testing with Dynamic Epistemic Logics

PART B: METHODOLOGICAL TOOLSETS

Discovering sociological knowledge through automated text analytics: Redefining the methodological foundations of sociology?

Combining scientific and non-scientific surveys to improve estimation and reduce costs

Harnessing the power of data science to grasp insights about human behavior, thinking and feeling from social media images

Computational modeling of characteristics conceptualized in an oppositional structure

PART C: NEW LOOK ON OLD ISSUES ? RESEARCH DOMAINS REVISITED BY COMPUTATIONAL SOCIAL SCIENCE

Modeling gender (im)balance in the Big Data era: A novel spatio-temporal approach to latent variables

Agent-based organizational ecologies: Algorithmic approaches to study market structuration and evolution

"Who is your best friend in the Politburo?" Possibilities and restrictions of historical network research

Participatory budgeting algorithms

From Durkheim to machine learning: Finding the relevant sociological content related to in a social media discourse

EPILOGUE

Changing understanding in algorithmic societies: Exploring a new perception of social reality with Computational Social Science