I like big, policy-relevant social science projects related to emerging media & tech. I’ve spent much of my career leading technical/research teams in industry, while managing to occasionally publish and participate in the public sphere.

Full bio below.

Interests
Education

  • PhD Political Communication

    Stanford University

  • MSc Statistics

    Stanford University

  • Intl MBA (Policy emphasis)

    UC San Diego (GPS)

  • BA in Political Science

    UC Santa Barbara

Featured Publications

A 2 million-person, campaign-wide field experiment shows how digital advertising affects voter turnout
We present the results of a massive, $8.9 million campaign-wide field experiment, conducted among 2 million moderate and …
Minali Aggarwal, Jennifer Allen, Alexander Coppock, Dan Frankowski, Solomon Messing, Kelly Zhang, James Barnes, Andrew Beasley, Harry Hantman, Sylvan Zheng
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Projecting confidence: How the probabilistic horse race confuses and demobilizes the public
Recent years have seen a dramatic change in horserace coverage of elections in the U.S.—shifting focus from late-breaking poll …
Sean Westwood, Solomon Messing, Yphtach Lelkes
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Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods
Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a …
Justin Grimmer, Solomon Messing, Sean J Westwood
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Bias in the flesh: Skin complexion and stereotype consistency in political campaigns
There is strong evidence linking skin complexion to negative stereotypes and adverse real-world outcomes. We extend these findings to …
Solomon Messing, Maria Jabon, Ethan Plaut
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Exposure to ideologically diverse news and opinion on Facebook
Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence …
Eytan Bakshy, Solomon Messing, Lada A Adamic
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Recent Posts

An Early Election 2024 Forecast
Early projections for 2024 based on previous Presidential and House returns slighly favor Republicans. These projections are completely unrelated to Biden’s recent polling numbers. Here’s the story behind this approach: In early 2020, I ran battleground state election forecasts for Acronym.
Sol Messing
Jan 11, 2024 7 min read
An Early Election 2024 Forecast
Disaggregating 'Ideological Segregation'
TLDR: [UPDATED SEPT 30] Yesterday, Science published a letter I wrote arguing that there is little evidence of algorithmic bias in Facebook’s feed ranking system that would serve to increase ideological segregation, also known as the “Filter Bubble” hypothesis.
Sol Messing
Aug 2, 2023 20 min read
Disaggregating 'Ideological Segregation'
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Projects

Projecting Confidence
How the Probabilistic Horse Race Demobilizes the Public
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Projecting Confidence
Facebook Condor URLs Data Release
Largest ever social science data set, released under differential privacy
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Facebook Condor URLs Data Release
Impression of Influence
A book on how Members of Congress use Credit Claiming
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Impression of Influence

Media

Radio/Podcasts/Video

Op-Eds

Media Clips

Tweet threads I like

Bio

Sol Messing is a Research Associate Professor at New York University, with the Center for Social Media and Politics. Prior to joining NYU, Messing founded data science research teams at Pew Research Center, Acronym, and Twitter. He has industry experience working on recommender systems, complex experimentation, feature engineering/discovery, algorithm audits, and differential privacy.

Messing’s published work on politics and digital media spans advertising, elections, social media, and Congressional communication. His most widely cited work shows how ranking and social signals dominate ideological signals in digital media, and how the networks we form more powerfully govern exposure to ideologically diverse content than algorithmic bias or individal preference. His research on Congress consists of numerous works on inter-party criticism and conflict among members on social media, as well as journal articles and a co-authored book, The Impression of Influence (Princeton University Press, 2014) about the impacts of credit-claiming on the voting public. He has also published work on the consequences of election forecasts and digital alterations to candidate images in advertising. Messing’s most recent work quantifies the impact of an entire digital Presidential advertising campaign in the 2020 election, in what is likely the largest digital advertsing field experiments in politics.

During the 2020 election cycle, Messing was Chief Scientist at ACRONYM, where his team drove investment by (correctly) modeling the electoral importance of Georgia, oversaw a meta-analytic ML system to generate estimates of persuasive messaging impact leveraging hundreds of past experiments and online behavioral data, and conducted the largest ever digital advertsing field experiment in politics.

At Facebook, Messing led the technical effort to release a differentially private data set reflecting more than an exabyte of media data, helped to found the Civic Integrity and FORT groups, and worked on prototypes that blend ML and experimentation (heterogenous effect estimation and contextual bandits).

Messing founded the Data Lab at Pew Research Center, where his team conducted algorithmic audits of Google Image searches and news photos on Facebook, used ML to study inauthentic and automated behavior on Twitter, and used NLP to understand the role of ideology and power structures embedded in how members of congress use social media to communicate.

Messing received his PhD in Communication from Stanford University in 2013, earning a Masters of Science in Statistics. He serves on the advisory board of Journal of Quantitative Description, served as Assistant Editor of Political Communication, and founded the Journal of International Policy Solutions.

Contact

Contact me via the form below or at firstname.lastname at gmail dot com.