Researcher reasoning meets computational capacity: Machine learning for social science
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 …
understand the social world. A special synergy is possible when social scientists combine …
The role of hyperparameters in machine learning models and how to tune them
Hyperparameters critically influence how well machine learning models perform on unseen,
out-of-sample data. Systematically comparing the performance of different hyperparameter …
out-of-sample data. Systematically comparing the performance of different hyperparameter …
[图书][B] Text as data: A new framework for machine learning and the social sciences
A guide for using computational text analysis to learn about the social world From social
media posts and text messages to digital government documents and archives, researchers …
media posts and text messages to digital government documents and archives, researchers …
Machine learning predictions as regression covariates
In text, images, merged surveys, voter files, and elsewhere, data sets are often missing
important covariates, either because they are latent features of observations (such as …
important covariates, either because they are latent features of observations (such as …
The solutionist ethic and the spirit of digital capitalism
O Nachtwey, T Seidl - Theory, Culture & Society, 2024 - journals.sagepub.com
Digital technologies are rapidly transforming economies and societies. Scholars have
approached this rise of digital capitalism from various angles. However, relatively little …
approached this rise of digital capitalism from various angles. However, relatively little …
[图书][B] Learning to quantify
This open access book provides an introduction and an overview of learning to quantify (aka
“quantification”), ie the task of training estimators of class proportions in unlabeled data by …
“quantification”), ie the task of training estimators of class proportions in unlabeled data by …
Using imperfect surrogates for downstream inference: Design-based supervised learning for social science applications of large language models
In computational social science (CSS), researchers analyze documents to explain social
and political phenomena. In most scenarios, CSS researchers first obtain labels for …
and political phenomena. In most scenarios, CSS researchers first obtain labels for …
Text as data methods for education research
Recent advances in computational linguistics and the social sciences have created new
opportunities for the education research community to analyze relevant large-scale text data …
opportunities for the education research community to analyze relevant large-scale text data …
Uncertainty-aware generative models for inferring document class prevalence
K Keith, B O'Connor - Proceedings of the 2018 Conference on …, 2018 - aclanthology.org
Prevalence estimation is the task of inferring the relative frequency of classes of unlabeled
examples in a group—for example, the proportion of a document collection with positive …
examples in a group—for example, the proportion of a document collection with positive …
How do social media users and journalists express concerns about social media misinformation? A computational analysis
Implications While social media misinformation constitutes a small percentage of an average
American's information diet in volume (Allen et al., 2020; González-Bailón et al., 2023; …
American's information diet in volume (Allen et al., 2020; González-Bailón et al., 2023; …