Publications

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
Publication date: 
07/2018
Authors: 
Philipp Hunziker
Julian Wucherpfennig
Aya Kachi
Nils-Christian Bormann

Very large spatio-temporal lattice data are becoming increasingly common across a variety of disciplines. However, estimating interdependence across space and time in large areal datasets remains challenging, as existing approaches are often (i) not scalable, (ii) designed for conditionally Gaussian outcome data, or (iii) are limited to cross-sectional and univariate outcomes.

Publication date: 
06/2018
Authors: 
Joshua A. Miller
Daniel Levine

In this article, we summarize the first civic game contest, its rules and process, and the results. We describe civics, games and argue that there is a fruitful intersection to be had between those two fields. Finally, we introduce the winning games.

Keywords: 
games
civics
democracy
refugees
community organizing
dialogue and deliberations
Publication date: 
06/2018
Authors: 
Xingshan Zeng
Jing Li
Lu Wang
Nick Beauchamp
Sarah Shugars
Kam-Fai Wong

Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation.

Publication date: 
04/2018
Authors: 
William Hobbs
Margaret E. Roberts

Conventional wisdom assumes that increased censorship will strictly decrease access to information. We delineate circumstances when increases in censorship expand access to information for a substantial subset of the population.

Publication date: 
04/2018
Authors: 
Nir Grinberg

Prior work established the benefits of server-recorded user engagement measures (e.g. clickthrough rates) for improving the results of search engines and recommendation systems. Client-side measures of post-click behavior received relatively little attention despite the fact that publishers have now the ability to measure how millions of people interact with their content at a fine resolution using client-side logging.

Keywords: 
User engagement
Online news
Information gain
Reading
Publication date: 
03/2018
Authors: 
David Lazer
Matthew A. Baum
Yochai Benkler
Adam J. Berinsky
Kelly M. Greenhill
Filippo Menczer
Miriam J. Metzger
Brendan Nyhan
Gordon Pennycook
David Rothschild
Michael Schudson
Steven A. Sloman
Cass R. Sunstein
Emily A. Thorson
Duncan J. Watts
Jonathan L. Zittrain

The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed.

Publication date: 
11/2017
Authors: 
Yu-Ru Lin
Ryan Kennedy
David Lazer

We examine the social antecedents of contributing to campaigns, with a particular focus on the role of population density and social networking opportunities.

Keywords: 
Fundraising
social networks
campaigns
spatial models
Publication date: 
09/2017
Authors: 
Kenny Joseph
William Hobbs
David Lazer
Oren Tsur

Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results.

Publication date: 
09/2017
Authors: 
Daizhuo Chen
Samuel Fraiberger
Robert Moakler
Foster Provost

Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users’ personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications.

Keywords: 
predictive modeling; transparency; privacy; comprehensibility; inference; control

Publications by type

Journal Article