Publications

Publication date: 
09/2020
Authors: 
Briony Swire Thompson
Joe DeGutis
David Lazer

One of the most concerning notions for science communicators, fact-checkers, and advocates of truth, is the backfire effect; this is when a correction leads to an individual increasing their belief in the very misconception the correction is aiming to rectify.

Publication date: 
06/2020

Agent-based models present an ideal tool for interrogating the dynamics of communication and exchange. Such models allow individual aspects of human interaction to be isolated and controlled in a way that sheds new insight into complex behavioral phenomena. This approach is particularly valuable in settings beset by confounding factors and mixed empirical evidence.

Publication date: 
05/2020
Authors: 
Jason Radford
Kenny Joseph

Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of issues have been identified with the machine learning models used to analyze social data.

Keywords: 
machine learning
computational social science
machine learning and social science
bias
fairness
Publication date: 
12/2019
Authors: 
David Lazer

The internet has become a popular resource to learn about health and to investigate one's own health condition. However, given the large amount of inaccurate information online, people can easily become misinformed. Individuals have always obtained information from outside the formal health care system, so how has the internet changed people's engagement with health information?

Keywords: 
misinformation
fake news
misconceptions
health
social media
Publication date: 
07/2019
Authors: 
Kenny Joseph
Briony Swire Thompson
Hannah Masuga
Matthew A. Baum
David Lazer

Using both survey- and platform-based measures of support, we study how polarization manifests for 4,313 of President Donald Trump’s tweets since he was inaugurated in 2017. We find high levels of polarization in response to Trump’s tweets.

Publication date: 
03/2019
Authors: 
Sarah Shugars
Nick Beauchamp

Individuals acquire increasingly more of their political information from social media, and ever more of that online time is spent in interpersonal, peer-to-peer communication and conversation. Yet, many of these conversations can be either acrimoniously unpleasant or pleasantly uninformative. Why do we seek out and engage in these interactions? Who do people choose to argue with, and what brings them back to repeated exchanges?

Keywords: 
politics
social media
interpersonal communication
deliberation
polarization
natural language processing
Publication date: 
01/2019
Authors: 
Nir Grinberg
Kenny Joseph
Lisa Friedland
David Lazer

The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated.

Publication date: 
12/2018
Authors: 
Michael Neblo
Kevin Esterling
David Lazer

OPINION — At the end of the movie “The Candidate,” Robert Redford’s character wins a Senate seat, and then immediately pulls aside his most trusted adviser and asks, “What do we now?” After the divisive election of 2018, we imagine that many newly elected members of Congress are pondering the same question.

Publication date: 
11/2018
Authors: 
Ronald Robertson
Shan Jiang
Kenny Joseph
Lisa Friedland
David Lazer
Christo Wilson

There is a growing consensus that online platforms have a systematic influence on the democratic process. However, research beyond social media is limited. In this paper, we report the results of a mixed-methods algorithm audit of partisan audience bias and personalization within Google Search.

Publication date: 
08/2018
Authors: 
Ethan Bernstein
Jesse Shore
David Lazer

People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a reduction in individual exploration for novel answers) relative to independent problem solving.

Keywords: 
collective intelligence
social influence
social networks

Publications by type

Journal Article