ReviewSocial science research2022
Researcher reasoning meets computational capacity: Machine learning for social science.
Review in Social science research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- In Defense of AI as an 'Author' Ethical Implications for Scientific Authors, Reviewers, Editors, and Publishing Policies.Head and neck pathology · 2026Review
- Structured Inequality, Uncertain Lifespans: Demographic Perspectives on Predicting Individual-Level Longevity.Population and development review · 2026Article
- Mapping subnational gender gaps in internet and mobile adoption using social media data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Temporal analysis of non-communicable diseases and NCD-HIV/AIDS comorbidity in Malawi: A 4-year retrospective study 2020-2022.Tropical medicine & international health : TM & IH · 2025Article
- Measuring vaccination coverage and concerns of vaccine holdouts from web search logs.Nature communications · 2024Article
- REFORMS: Consensus-based Recommendations for Machine-learning-based Science.Science advances · 2024Review
- Disparate Effects of Disruptive Events on Children.The Russell Sage Foundation journal of the social sciences : RSF · 2024Article
- Recent Developments in Causal Inference and Machine Learning.Annual review of sociology · 2023Article
- Literature Review on Health Emigration in Rare Diseases-A Machine Learning Perspective.International journal of environmental research and public health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning amplifies researcher coding, summarizes complex data, relaxes statistical assumptions, and targets researcher attention to further social science research. We aim to reduce perceived barriers to machine learning by summarizing several fundamental building blocks and their grounding in classical statistics. We present a few guiding principles and promising approaches where we see particular potential for machine learning to transform social science inquiry. We conclude that machine learning tools are increasingly accessible, worthy of attention, and ready to yield new discoveries for social research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.