Chapter 15: @kristinalerman.bsky.social on the Strong Friendship Paradox.
Showing how the structure of social networks systematically biases our perceptions of reality. When our friends are not representative of the population, rare behaviours and opinions can appear surprisingly common.
Taha Yasseri
Chapter 14: @bolozna.bsky.social on multilayer social networks.
A great introduction to one of the most important recent developments in network science: moving beyond single-layer graphs to represent the multiple social contexts and relationships that shape human behavior.
Social life is layered!
Taha Yasseri
Chapter 13: @jsaramak.bsky.social & @pholme.bsky.social on temporal networks of social interactions.
A fascinating chapter showing why static network snapshots often miss the most important thing: the timing and order of interactions.
Social networks are dynamic processes, not just fixed structures.
Taha Yasseri
Chapter 12: @fedebotta.bsky.social from @exetercompsci.bsky.social on online images and computational social science.
A fascinating chapter on how images shared online can help us study politics, misinformation, culture, & collective behaviour and why visual data deserves a much bigger place in CSS.
Taha Yasseri
Chapter 11: Luis-Daniel Ibáñez, Johanna Walker & @elenasimperl.bsky.social on open data in computational social science. Why open data is not just a technical issue, but also a question of sustainability, impact, politics, and bias. Useful data is more than available data.
@aiatkings.bsky.social
Taha Yasseri
Chapter 10: @feloe.bsky.social & @vanatteveldt.com
on social media data donation and digital tracking.
A very useful chapter on how digital traces can be integrated into social science research, not as a replacement for surveys and other methods, but as a powerful complement.
Taha Yasseri
Chapter 9: Kiran Garimella on using WhatsApp data for computational social science.
An important chapter on why WhatsApp deserves far more attention in CSS, not only because of its scale, but because it opens a window onto digital life beyond the usual Western, open-platform focus.
Taha Yasseri
Chpater 8: @dirkhovy.bsky.social, M Gerondeau & J Globisz on text data and natural language processing.
A very useful chapter on why text is such a rich source for CSS, and how NLP can help with exploration, prediction, and generation; if used thoughtfully and with clear research goals.
Taha Yasseri
Chapter 7: @kwelle.bsky.social of @gesis.org & @indiiigo.bsky.social of Mannheim on using social media data in CSS. Social media provides rich digital traces of human behavior, but also raises serious methodological & ethical challenges, from platform bias to reproducibility. A great guide!
Taha Yasseri
Chapter 6: @lauraknelson.bsky.social from UBC Sociology on computational inductive research.
The chapter makes a strong case that unsupervised methods don’t replace theory: they reshape how we generate & validate it.
Induction, abduction, deduction, combined with transparency & reproducibility.
Taha Yasseri
Chapter 5: @martinarvidsson.bsky.social, Hedström, Jarvis & @marckeuschnigg.bsky.social on the intersection of Analytical Sociology and Computational Social Science.
Computational tools are most powerful when used to identify and test mechanisms, not just describe or predict aggregate patterns.
Taha Yasseri
In Chapter 4, Ralph Schroeder of @oii.ox.ac.uk argues that CSS needs a stronger theory, not just better data.
He proposes an AI-driven model of media agenda-setting that links media visibility, public attention, and political change. A big step toward cumulative media theory.
Taha Yasseri
Chapter 3: @helenmargetts.bsky.social & Cosmina Dorobantu on how CSS can improve public policymaking, from better detection and prediction to ethical, accountable use of AI in government. A powerful case for impact beyond academia. doi.org/10.4337/9781...
@oii.ox.ac.uk @lsedatascience.bsky.social
Taha Yasseri
Chapter 2: Duncan Watts & @davidlazer.bsky.social reflect on how the field has evolved from early simulation models to today’s large-scale data and experiments, and ask how can computational social science move from producing insights to having real societal impact? @nunetsi.bsky.social
Taha Yasseri
Thrilled to announce the Handbook of Computational Social Science is officially out! 956 pages, 118 authors, and truly global, interdisciplinary perspectives. Deep thanks to the contributors and anonymous reviewers who shaped this over 4 years. Buy your copy now!
@elgarpublishing.bsky.social
