Computational social science
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A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.
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The emergence of Computational Social Science (CSS) marks a transformative shift in the study of social phenomena, triggered by advancements in computational technology and large-scale collection of digital behavioral trace data.
“The emergence of Computational Social Science (CSS) marks a transformative shift in the study of social phenomena, triggered by advancements in computational technology and large-scale collection of (digital) behavioral trace data.”
From Computational Social Science of Social Cohesion and Polarization (2026) · cites this paper as Lazer et al. 2009
In “Life in the network” and “From individuals to societies” (page not provided), the paper links emerging computational social science to new technologies and large-scale digital records of behavior. It describes a transformation in understanding as a potential outcome, however, and says barriers may limit progress; the claim presents the shift more definitively than the source does. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe new millennium has witnessed the Computational Social Science (CSS) revolution.
“The new millennium has witnessed the Computational Social Science (CSS) revolution (Lazer et al. 2009;Conte et al. 2012;Lazer et al. 2020).”
From Computational Social Science of Social Cohesion and Polarization (2026) · cites this paper as Lazer et al. 2009
In “The coming age of computational social science,” the paper says computational social science is emerging and occurring on a large scale in some organizations. But it describes its emergence in academic social science as slow and much of its transformative potential as prospective, rather than saying a CSS revolution had already occurred. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSocial media platforms are increasingly used to gather information about societal issues and breaking news developments.
“Social media platforms are increasingly being used as a tool for gathering information about, for example, societal issues (Lazer et al. 2009), and to find out about the latest developments during breaking news stories (Phuvipadawat and Murata 2010).”
From Detection and Resolution of Rumours in Social Media (2019) · cites this paper as Lazer et al. 2009
The social web enables access to social traces at a scale and level of detail, both in breadth and depth, impractical with conventional data collection techniques, like surveys or user studies.
“The social web enables access to social traces at a scale and level of detail, both in breadth and depth, impractical with conventional data collection techniques, like surveys or user studies (Richardson, 2008; Lazer et al., 2009).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Lazer et al., 2009
Digital trace data-from social media posts to behavioral logs-provide an empirical foundation for tracking the evolution of social alignments at scale.
“Digital trace data-from social media posts to behavioral logs-provide an empirical foundation for tracking the evolution of social alignments at scale (Lazer et al. 2009).”
From Computational Social Science of Social Cohesion and Polarization (2026) · cites this paper as Lazer et al. 2009
Urban science/informatics offers potential for urban knowledge with greater breadth, depth, scale and timeliness, and is inherently longitudinal, in contrast to traditional urban studies.
“Urban science/informatics, it is posited, offers the potential for urban knowledge that has greater breadth, depth, scale and timeliness, and is inherently longitudinal, in contrast with that derived from longer standing, more traditional urban studies [3,15].”
From The ethics of smart cities and urban science (2016) · cites this paper as [15]
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- Authors
- Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M.
- Affiliations
- Harvard University (US)
- Massachusetts Institute of Technology (US)
- University of Michigan (US)
- New York University (US)
- Northeastern University, Boston, MA, USA.
- Interdisciplinary Scientific Research (US)
- Northwestern University (US)
- University of California San Diego (US)
- Columbia University (US)
- Cornell University (US)
- Massachusetts Institute of Technology, Cambridge, MA, USA.
- ORCID
- Venue
- Science
- Published
- 2009-02-06
- Volume
- 323
- Issue
- 5915
- Pages
- 721-723
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC2745217
- OpenAlex
- W2159397589
- Cited by
- 3,365
- References
- 46
- Citations by year
- 2026: 158 · 2025: 135 · 2024: 122 · 2023: 174 · 2022: 160 · 2021: 220 · 2020: 280 · 2019: 248 · 2018: 236 · 2017: 256 · 2016: 231 · 2015: 274 · 2014: 263 · 2013: 221 · 2012: 158
- FWCI
- 691.10
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institute on Aging (P01AG031093)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P01HD045753)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Keywords
- computational social science, large-scale data collection, data analysis, individual and group behavior, behavioral patterns
- MeSH terms
- Data Collection, Group Processes, Humans, Interpersonal Relations, Research, Social Behavior, Social Sciences, Privacy, Internet
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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