How public and private Twitter users in the U.S. compare — and why it might matter for your research
Overall, our survey found that 13% of U.S. adult Twitter users keep their feeds private.
A behind-the-scenes blog about research methods at Pew Research Center.
For our latest findings, visit pewresearch.org.
Data Science
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Working with geography in survey data
Survey researchers frequently explore differences in public opinion by demographic group — how men’s views compare with those of women, for example, or how younger people compare with older people. Often, it’s also possible to look at differences by survey respondents’ geographic location. Yet, when geographic information is available in survey data, it’s not always […]
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Overcoming the limitations of topic models with a semi-supervised approach
In this post, I’ll show how to overcome some of challenges that arise with topic modeling with what’s known as a “semi-supervised” approach.
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Crowdsourcing a video coding project can be as effective as using trained research assistants
Measuring the content and meaning of videos is an important research goal for social scientists, but the tools for analyzing them at scale lag behind new advances.
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How social scientists can use transfer learning to kick-start a deep learning project
Social scientists are increasingly adopting machine learning methods to analyze large amounts of text, images and other kinds of data.
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Sharing the code we used to study the public’s interest in the Flint water crisis
In 2017, Pew Research Center published an analysis of Google search trends related to the water crisis in Flint, Michigan. The analysis…
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Q&A: What works and what doesn’t when studying social media data
Ever since Friendster and Myspace gained popularity in the early 2000s, social scientists have been interested in studying the impact of…
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Using supervised machine learning to quantify political rhetoric
Several posts on this blog have examined unsupervised methods of natural language processing. These algorithms and models can help…
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Making sense of topic models
Topic models can produce clusters of words that characterize written documents. But how do we figure out what those clusters mean, exactly?
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An intro to topic models for text analysis
Topic models can scan documents, examine words and phrases within them, and “learn” groups of words that characterize those documents.
