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Articles by Solomon Messing
The train has left the station: Agentic AI and the future of social science research
A viral blog post recently compared the current moment in artificial intelligence (AI) to February 2020, just before COVID-19 turned the world upside down.
Was there censorship on TikTok after the U.S. takeover?
Photo by Zulfugar Karimov on Unsplash. Recently, there have been allegations that TikTok has censored content on the platform after the Jan. 22 restructuring that shifted TikTok to a U.S.-based joint venture. More specifically, people alleged that certain types of content were no longer being accepted for posting on the platform or were receiving less engagement. We conducted an over-time analysis of posts with particular keywords on TikTok before and after the restructuring.
Misunderstood mechanics: How AI, TikTok, and the liar's dividend might affect the 2024 elections
It’s been over a year since the release of OpenAI’s ChatGPT launched an international conversation about how generative artificial intelligence—i.e., AI capable of producing text, images, video, and more—could transform our lives.
Are algorithmic bias claims supported?
AAAS login provides access to Science for AAAS Members, and access to other journals in the Science family to users who have purchased individual subscriptions. Download and print this article for your personal scholarly, research, and educational use. Buy a single issue of Science for just $15 USD.
Looking at the Evidence on Apple Podcasts
Alex and Evelyn get Josh Tucker, a professor at New York University, and Solomon Messing, a Visiting Researcher Georgetown University, to talk them through the results of two major studies about the effects of online speech; they also discuss Twitter cutting off API access; public universities cutting off TikTok access; Apple promising more transparency about app store take-downs, and an update from the courtrooms.
A 2 million-person, campaign-wide field experiment shows how digital advertising affects voter turnout - Nature Human Behaviour
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Abstract We present the results of a large, US$8.9 million campaign-wide field experiment, conducted among 2 million moderate- and low-information persuadable voters in five battleground states during the 2020 US presidential election. Treatment group participants were exposed to an 8-month-long advertising programme delivered via social media, designed to persuade people to vote against Donald Trump and for Joe Biden.
By Minali Aggarwal, Jennifer Allen, Dan Frankowski, Solomon Messing, Kelly Zhang, Andrew Beasley Verified
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Nature
Verified
The “Haha Ratio”: Learning from Facebook’s Emoji Reactions to Predict Persuasion Effects of Political Ads
By Minali Aggarwal, Sylvan Zheng, Sol Messing, Dan Frankowski, James Barnes, & the Barometer Team at ACRONYM Do messages criticizing Donald Trump’s performance on Covid-19 reduce his support among swing voters? What if the message comes from a conservative personality, like Tucker Carlson? It was our job at ACRONYM to answer questions like these during the lead up to the 2020 election.
Past vote data outperformed the polls. How did it go so wrong? | Sol Messing
It’s becoming clear that the 2020 polls underestimated Trump’s support by anywhere from a 4–7 point margin-a significantly worse miss than in 2016, when state polls were off but the national polls did relatively well. In fact, this year we were better off using projections based on past vote history in each state to predict how things would go in battleground states, as I’ll show below. But I also want to start to formulate questions about what happened this time around.
Election forecasts helped elect Trump in 2016. It could happen again in 2020.
Fall of 2020 will find many of us living in a state of fear and social isolation, trying to get by after losing a job, or helping anxious children get through distance learning. With all the pandemic has meant for Americans, how many will brave unprecedented long lines or successfully navigate all the steps required to vote by mail? Some may not vote after hearing about the latest election forecasts, which give Joe Biden anywhere from a 71 to 99.5% chance of winning.
Projecting Confidence: How the Probabilistic Horse Race Confuses and Demobilizes the Public
No Access Abstract Recent years have seen a dramatic change in horse-race coverage of elections in the United States—shifting focus from late-breaking poll numbers to sophisticated meta-analytic forecasts that emphasize candidates’ chance of victory. Could this shift in the political information environment affect election outcomes? We use experiments to show that forecasting increases certainty about an election’s outcome, confuses many, and decreases turnout.
Why Trump’s chances are better than they look
Sol Messing is Chief Scientist at Acronym, Affiliated Researcher at Georgetown. According to the latest polling research, Trump’s chances of hanging on to power beyond 2020 look pretty dismal. Nate Cohn published an impressive battleground poll from New York Times/Sienna showing Biden ahead of Trump by at least six points in pivotal states.
Projecting Confidence: How the Probabilistic Horse Race Confuses and Demobilizes the Public
Abstract Recent years have seen a dramatic change in horse-race coverage of elections in the United States—shifting focus from late-breaking poll numbers to sophisticated meta-analytic forecasts that emphasize candidates’ chance of victory. Could this shift in the political information environment affect election outcomes? We use experiments to show that forecasting increases certainty about an election’s outcome, confuses many, and decreases turnout.
Preserving privacy while fostering meaningful research on elections and democracy
Social Science One and Facebook this week hosted training for more than 50 independent researchers announced last month by the Social Science Research Council to study the role of social media in elections and democracy.
Exposure to ideologically diverse news and opinion on Facebook
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Exposure to Diverse Information on Facebook
As people increasingly turn to social networks for news and civic information, questions have been raised about whether this practice leads to the creation of “echo chambers,” in which people are exposed only to information from like-minded individuals [2]. Other speculation has focused on whether algorithms used to rank search results and social media posts could create “filter bubbles,” in which only ideologically appealing content is surfaced [3].
How to break regression
(Photo illustration by Pew Research Center and iStock.com/Eshma) Regression models are a cornerstone of modern social science. They’re at the heart of efforts to estimate causal relationships between variables in a multivariate environment and are the basic building blocks of many machine learning models. Yet social scientists can run into a lot of situations where regression models break.
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Twitter Bots: An Analysis of the Links Automated Accounts Share
An estimated two-thirds of tweeted links to popular websites are posted by automated accounts – not human beings CORRECTION (April 2018): In the original report, the words “liberals” and “conservatives” were reversed in one sentence.
Methodology
This report examines how often automated accounts share links on social media, using a large collection of links from Twitter users collected between July 27 and Sept. 11, 2017. The report relies upon several sources of data and various measures. This methodology section provides an overview of how data were collected and how measures were generated. Link: A web URL to an article or main page of a website.
Twitter Bots: An Analysis of the Links Automated Accounts Share
CORRECTION (April 2018): In the original report, the words “liberals” and “conservatives” were reversed in one sentence.
Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook
We demonstrate that social media data represent a useful resource for testing models of legislative and individual-level political behavior and attitudes. First, we develop a model to estimate the ideology of politicians and their supporters using social media data on individual citizens’ endorsements of political figures. Our measure allows us to place politicians and more than 6 million citizens who are active in social media on the same metric.
Analysis | How election forecasts confuse Americans — and may lead them not to vote at all
Where were you on the night of Nov. 8, 2016? If you’re like many political junkies, you were watching election night coverage and wondering not whether Hillary Clinton or Donald Trump would win but might Clinton do so well that she’d win in places like Texas and Arizona. When she lost, many on both sides of the aisle were shocked. After all, forecasters gave her odds of winning that ranged from 70 to 99 percent.
Use of election forecasts in campaign coverage can confuse voters and may lower turnout
Americans have long been familiar with “horse race” polls that accompany elections in the United States. But since 2008, a new polling tool has gained prominence, one that not only suggests which candidate is ahead at any given moment but also estimates their probability of winning the eventual election.
Very liberal or conservative legislators most likely to share news on Facebook
The most ideological members of Congress shared news stories on their Facebook pages more than twice as often as moderate legislators between Jan. 2, 2015, and July 20, 2017, according to a new Pew Research Center study that examined all official Facebook posts created by members of Congress in this period. The analysis included links to national news websites that members of Congress linked to at least 25 times overall in the time studied.
Very liberal or conservative legislators most likely to share news on Facebook
The most ideological members of Congress shared news stories on their Facebook pages more than twice as often as moderate legislators between Jan. 2, 2015, and July 20, 2017, according to a new Pew Research Center study that examined all official Facebook posts created by members of Congress in this period. The analysis included links to national news websites that members of Congress linked to at least 25 times overall in the time studied.
Sharing the News in a Polarized Congress
Partisan and ideological divides shape which news outlets legislators share links to on Facebook This report describes findings from an analysis of all Facebook posts issued by members of the 114th and 115th Congresses between Jan. 2, 2015, and July 20, 2017. Researchers collected Facebook posts from members’ official Facebook accounts using the social media platform’s application programming interface (API) for public pages.
Sharing the News in a Polarized Congress
By Solomon Messing, Patrick van Kessel and Adam Hughes This report describes findings from an analysis of all Facebook posts issued by members of the 114th and 115th Congresses between Jan. 2, 2015, and July 20, 2017. Researchers collected Facebook posts from members’ official Facebook accounts using the social media platform’s application programming interface (API) for public pages. Researchers obtained 447,684 posts from 581 unique members of Congress across the two most recent Congresses.
After Las Vegas attack, Democrats in Congress were far more likely than Republicans to mention guns on Facebook
The issue of gun policy returned to the political front burner following the Oct. 1 mass shooting in Las Vegas. And in the week after the attack, partisan differences were on full display in how elected officials responded on Facebook: Among Democratic members of Congress who posted about the attack, 63% mentioned guns, compared with only 2% of congressional Republicans, according to a Pew Research Center analysis.
Exposure to ideologically diverse news and opinion on Facebook
Report Eytan Bakshy1,*,†, Solomon Messing1,†, Lada A. Adamic1,2 1Facebook, Menlo Park, CA 94025, USA. 2School of Information, University of Michigan, Ann Arbor, MI, USA. ↵*Corresponding author. E-mail: ebakshy{at}fb.com ↵† These authors contributed equally to this work. See allHide authors and affiliations Science 05 Jun 2015: Vol. 348, Issue 6239, pp. 1130-1132 DOI: 10.1126/science.aaa1160 Not getting all sides of the news?
What color is Obama? These researchers examined reactions when his skin looks darker.
Did the 2008 John McCain campaign deliberately manipulate the color of Barack Obama’s skin in TV ads, trying to evoke racial biases? Several major media outlets have made this allegation in stories about our recent research. If true, this would be further evidence that American political campaigns have, as some observers allege, long been making subtle but intentional appeals to racism. But what our study found was more nuanced. We’d like to explain.
Exposure to ideologically diverse news and opinion on Facebook
People are increasingly turning away from mass media to social media as a way of learning news and civic information. Bakshy et al. examined the news that millions of Facebook users' peers shared, what information these users were presented with, and what they ultimately consumed (see the Perspective by Lazer). Friends shared substantially less cross-cutting news from sources aligned with an opposing ideology.
Exposure to ideologically diverse news and opinion on Facebook
Exposure to news and civic information is increasingly mediated by online social networks and personalization (1). Information abundance provides individuals with an unprecedented number of options, shifting the function of curating content from newsroom editorial boards to individuals, their social networks, and manual or algorithmic information sorting (2–4).
Data Science for Social Good
When Burkina Faso beauty salon owner Korotimi Sere needed a loan, she had few options. For Korotimi and millions like her in Sub-Saharan Africa, getting a conventional loan is nearly impossible--80% of people in this region do not have a bank account and those that do often must pay interest rates upward of 50%. To help solve this problem, Civic minded data scientists from Bayes Impact partnered with microfinance nonprofit Zidisha.
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