Author Archives: Solomon

About Solomon

Political Scientist, Facebook Data Science

Response to FiveThirtyEight’s Podcast about our paper, “Projecting confidence”

Do you remember the night of Nov 8, 2016? I was glued to election coverage and obsessively checking probabilistic forecasts, wondering whether Clinton might do so well that she’d win in places like my home state of Arizona. Although FiveThirtyEight had … Continue reading

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Replication of Study 2 in Bias in the Flesh: Skin Complexion and Stereotype Consistency in Political Campaigns

Replication of Study 2 in “Bias in the Flesh: Skin Complexion and Stereotype Consistency in Political Campaigns” This post presents a replication of Messing et al. (2016, study 2), which showed that exposure to darker images of Barack Obama increased stereotype … Continue reading

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Exposure to Ideologically Diverse News and Opinion, Future Research

Eytan Bakshy & Solomon Messing Earlier this month, we published an early access version of our paper in ScienceExpress (Bakshy et al. 2015), “Exposure to ideologically diverse news and opinion on Facebook.” The paper constitutes the first attempt to quantify … Continue reading

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When to Use Stacked Barcharts?

Yesterday a few of us on Facebook’s Data Science Team released a blogpost showing how candidates are campaigning on Facebook in the 2014 U.S. midterm elections. It was picked up in the Washington Post, in which Reid Wilson calls us “data … Continue reading

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Visualization Series: Using Scatterplots and Models to Understand the Diamond Market

My last post railed against the bad visualizations that people often use to plot quantitive data by groups, and pitted pie charts, bar charts and dot plots against each other for two visualization tasks.  Dot plots came out on top. … Continue reading

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Streamline Your Mechanical Turk Workflow with MTurkR

I’ve been using Thomas Leeper‘s MTurkR package to administer my most recent Mechanical Turk study—an extension of work on representative-constituent communication claiming credit for pork benefits, with Justin Grimmer and Sean Westwood.  MTurkR is excellent, making it quick and easy to: test … Continue reading

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Generating Labels for Supervised Text Classification using CAT and R

The explosion in the availability of text has opened new opportunities to exploit text as data for research. As Justin Grimmer and Brandon Stewart discuss in the above paper, there are a number of approaches to reducing human text to … Continue reading

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