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.
PhD Political Communication
Intl MBA (Policy emphasis)
UC San Diego (GPS)
BA in Political Science
UC Santa Barbara
Behold, The Algorithm (or, parts of it, sort of) Moderated Content, hosted by Alex Stamos & Evelyn Douek
What can we learn from a $9 million social media advertising experiment? Differential Turnout and Early Voting Moderated Content, hosted by Alex Stamos & Evelyn Douek
CITP: Bringing Transparency to Digital Political Campaigns Symposium Center for Information Technology Policy (CITP) and the Center for the Study of Democratic Politics (CSDP)
Should We Stop Paying Attention To Election Forecasts? NPR’s Science Friday, hosted by Ira Flatow & Elah Feder
Data Science across Academia, Industry, and Progressive Campaigns, with Dr. Solomon Messing. Social Media and Politics Podcast, hosted by Michael Bossetta
Technology in Political Campaigns and Activism 2020 CODE@MIT - Fireside Panel, hosted by Dean Eckles
Using Trumps Tactics Against Him with The Leaders of Barometer. The Great Battlefield Podcast, hosted by Nathaniel G. Pearlman
A Look Beyond Traditional Audiences + Models with Solomon Messing FWIW 2020 Debrief, hosted by ACRONYM
What color is Obama? These researchers examined reactions when his skin looks darker. Washington Post’s Monkey Cage
How election forecasts confuse Americans — and may lead them not to vote at all. Washington Post’s Monkey Cage
Election forecasts helped elect Trump in 2016. It could happen again in 2020. USA Today
How a Digital Ad Strategy That Helped Trump Is Being Used Against Him. New York Times
Since Facebook helped elect Trump, some former employees are using similar digital tricks to give Democrats an edge in 2020. Vanity Fair
Did Trump win in 2016 because people are bad at probability? Washington Post
This is how FiveThirtyEight is trying to build the right amount of uncertainty into its 2020 election data analysis. NiemanLab
Americans Don’t Understand Probabilities – and It Could Affect Election Turnout. New York Magazine
Study Finds Election Forecasts Lower Voter Turnout. Political Wire
Kobe Bryant’s ’light-skinned’ remark hints at NBA’s peculiar racial politics. The Guardian
Study: 2008 McCain attack ads depicted Obama with darker skin tone. CBS News
Obama’s skin looks a little different in these GOP campaign ads. Washington Post
Study: Campaign Ads with Dark-Skinned Black People Appeal to White Racism. Atlanta Black Star
What can researchers find among the 32 million URLs Facebook just released to Social Science One? Poynter
Does Facebook drive political polarization? Data science and research. Harvard Shorenstein Center
Two-thirds of links on Twitter come from bots. The good news? They’re mostly bland. Vox
Most Links to Popular Sites on Twitter Come From Bots. Wired
Twitter bots are getting busy making sure your tweet goes viral. FastCompany
Bots on Twitter share two-thirds of links to popular websites. TechCrunch
Twitter bots are behind 66% of tweeted links for most popular sites. VentureBeat
Twitter bots rampant in news, porn and sports links. USA Today
Report: Bots Promote 66 Percent of Twittersphere Links. Observer
Think your articles are getting a lot of attention on Twitter? It could be a lot of posting by bots. NiemanLab
New study finds 66% of shared links on Twitter came from bots. Yahoo News
FiveThirtyEight Politics Podcast. FiveThirtyEight
Clinton’s Achilles’ heel in 2016 may have been overconfidence. Washington Post
Facebook reactions to posts by Democrats got a lot angrier after Trump was elected. Quartz
How politicians’ use of social media is reinforcing a partisan media divide. Washington Post
Lawmakers’ Facebook news feeds reflect political polarization. Politico
Republican lawmakers ‘go negative’ more often than Democrats, according to a first-of-its-kind analysis. Washington Post
Republicans Disagree With You, and They Disagree Indignantly. Mother Jones
Q&A with Solomon Messing of Pew Research Center’s Data Labs. Pew Research Center
Investigating the network: The top 10 articles from the year in digital news and social media research. NiemanLab
Facebook Use Polarizing? Site Begs to Differ. New York Times
Does Facebook’s News Feed control your world view?. CBS News
Don’t (just) blame Facebook: We build our own bubbles. Ars Technica
It’s mainly your fault that you click on things you already agree with. Washington Post
It’s The Frequency, Not The Size: Compromise & Credit Claiming in Congress. Mischiefs of Faction (Vox)
When it comes to policy, moderate politicians keep their mouths shut. Stanford News
The one thing that’s clear about polling in 2020 is this: the tech bros saying we need to use deep learning to forecast elections understand neither deep learning nor elections.— Sol Messing (@SolomonMg) November 14, 2020
Does @FiveThirtyEight *move markets?* After their real-time forecast had GOP's odds of taking the House spiking at 60% at ~8:15PM, @PredictIt's odds on the GOP rose above 50-50, & **U.S. government bond prices saw brief spike of 2-4 basis points.** pic.twitter.com/MFZ7vb1yxq— Sol Messing (@SolomonMg) November 11, 2018
When you only train your ML in the Bay Area... https://t.co/UVm9kBEVnE— Sol Messing (@SolomonMg) March 13, 2017
Today we published an Op Ed in @USATODAY calling on journalists, policymakers & public intellectuals not to play to clickbait horse-race commentary in the 2020 election. That game led some to stay home in 2016 and there's too much at stake this time. https://t.co/R0J1hg2vf2— Sol Messing (@SolomonMg) October 1, 2020
New post + thread: why Trump's chances are better than they now look https://t.co/j2lhem3CXw.— Sol Messing (@SolomonMg) June 28, 2020
Recent polling makes Trump's chances of winning in Nov look bad. Failing to account for COVID-19 + other issues, the polling itself could lower Dem turnout.
Social Media Researchers, you'll want to check this out -- perhaps the biggest data set describing sharing news and other content on social media ever released: https://t.co/V9ojjoJas6— Sol Messing (@SolomonMg) July 11, 2018
The media failed to question Cambridge Analytica’s marketing hype, which turned out to be mostly snake oil. Political scientists have been saying so for a long time. The UK ICO recently released an investigation that found... the same thing https://t.co/6AQt1gOCrZ pic.twitter.com/qsdBO9RGrS— Sol Messing (@SolomonMg) October 30, 2020
Thread: Here's what's not in this @CraigSilverman piece on Social Science One & FB: https://t.co/qsplIRa2PF— Sol Messing (@SolomonMg) August 23, 2019
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.