Publications by Year: 2016

The Civic Mission of MOOCs: Measuring Engagement across Political Differences in Forums
Reich, Justin, Brandon Stewart, Kimia Mavon, and Dustin Tingley. 2016. “The Civic Mission of MOOCs: Measuring Engagement across Political Differences in Forums”. Proceedings of the Third (2016) ACM Conference on Learning @ Scale 1-10. Publisher's VersionAbstract

In this study, we develop methods for computationally measuring the degree to which students engage in MOOC forums with other students holding different political beliefs. We examine a case study of a single MOOC about education policy, Saving Schools, where we obtain measures of student education policy preferences that correlate with political ideology. Contrary to assertions that online spaces often become echo chambers or ideological silos, we find that students in this case hold diverse political beliefs, participate equitably in forum discussions, directly engage (through replies and upvotes) with students holding opposing beliefs, and converge on a shared language rather than talking past one another. Research that focuses on the civic mission of MOOCs helps ensure that open online learning engages the same breadth of purposes that higher education aspires to serve.

A model of text for experimentation in the social sciences
Roberts, Margaret E., Brandon M. Stewart, and Edoardo M Airoldi. 2016. “A model of text for experimentation in the social sciences”. Journal of the American Statistical Association 111 (515):988-1003. Publisher's VersionAbstract

Statistical models of text have become increasingly popular in statistics and computer science as a method of exploring large document collections. Social scientists often want to move beyond exploration, to measurement and experimentation, and make inference about social and political processes that drive discourse and content. In this paper, we develop a model of text data that supports this type of substantive research.
Our approach is to posit a hierarchical mixed membership model for analyzing topical content of documents, in which mixing weights are parameterized by observed covariates. In this model, topical prevalence and topical content are specified as a simple generalized linear model on an arbitrary number of document-level covariates, such as news source and time of release, enabling researchers to introduce elements of the experimental design that informed document collection into the model, within a generally applicable framework. We demonstrate the proposed methodology by analyzing a collection of news reports about China, where we allow the prevalence of topics to evolve over time and vary across newswire services. Our methods quantify the effect of news wire source on both the frequency and nature of topic coverage.


NB: This is a revised version of the working paper previously titled "Structural Topic Models." SupplementReplication Package, Software

Navigating the Local Modes of Big Data: The Case of Topic Models
Roberts, Margaret E, Brandon M Stewart, and Dustin Tingley. 2016. “Navigating the Local Modes of Big Data: The Case of Topic Models”. in Computational Social Science: Discovery and Prediction. New York: Cambridge University Press. Publisher's Version

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