Journal of Quantitative Description: Digital Media

Andy Guess (Princeton U.)
Eszter Hargittai (U. of Zurich)
Kevin Munger (Penn State U.)

Coming in 2021!

For updates, follow us on Twitter @journalqd

JQD:DM is an open access, peer-reviewed scholarly journal hosted on the University of Zurich's HOPE platform. All articles will be freely available online immediately upon publication and we plan no submission or publication fees for at least the first two years, but hopefully longer.

The journal publishes quantitative descriptive social science. It does not publish research that makes causal claims.

The production of descriptive knowledge is currently undersupplied in quantitative social science. As John Gerring documents in the case of political science in his 2012 article "Mere Description", scholarship in the postwar era has seen a steady trend from description to causality. We applaud developments in method that discredit spurious causal research; this has led to a much-needed advance in rigor for claims of causation. As this shift illustrates, social science has a momentum that takes years to redirect, and graduate training, journal space, prestige, and grant funding have all been shifting away from quantitative description. Our hope is that this journal is the beginning of another course correction.

Descriptive knowledge is necessary for the following steps in the social science process:

Hypothesis generation: Trivially, we need to know what is before we can derive hypotheses about why it is or what it does. Too often, experiments are designed without first establishing the prevalence of the causes or effects being studied.

Topic importance: We want to study the most important questions, but relying on the intuitions of social scientists about "importance" is baldly unscientific. Quantitative description offers a framework for rigor.

Generalizability: The goal of many social scientists is to create "generalizable" knowledge. There are open questions about how best to do this, but an essential component of any generalizability project is the knowledge of how the target context differs from the known contexts. This knowledge is quantitative description, which thus serves as a complement to causal knowledge, enabling its applicability to novel contexts.

Notice that the scope of this initial incarnation of the JQD is restricted to Digital Media. The last few decades have seen the fastest increase in the production of human communication in history. JQD:DM will publish quantitative description of this increasingly massive sphere of online media.

A note on scope: The journal is aiming not just for digital trace data sets or structured datasets but evidence that speaks to some substantive question about communication processes and media. We will be working with a letter-of-intent model so that people can get feedback about the applicability of their piece for the journal before investing in writing up their material. Examples of relevant work include:

  • What segments of the population are using a particular platform?
  • What information sources do people use to learn about Covid-19?
  • Which political parties have the most engagement on social media?
  • What do different religious organizations communicate to their members about a particular topic?
  • Who is most likely to share videos about fake news?
  • What types of science articles have the most edits on Wikipedia?
  • What proportion of videos people share reference social justice topics?
Advisory Board

Sinan Aral, Massachusetts Institute of Technology
Axel Bruns, Queensland University of Technology
Nosh Contractor, Northwestern University
Paul DiMaggio, New York University
Jamie Druckman, Northwestern University
Bill Dutton, University of Southern California
Jeremy Freese, Stanford University
Darren Gergle, Northwestern University
John Gerring, University of Texas at Austin
Fabrizio Gilardi, University of Zurich
Shane Greenstein, Harvard University
Andrea Hollingshead, University of Southern California
David Lazer, Northeastern University
Suzie Linn, Pennsylvania State University
Neil Malhotra, Stanford University
Solomon Messing, Acronym
Rasmus Kleis Nielsen, University of Oxford
Pippa Norris, Harvard University
Brendan Nyhan, Dartmouth College
Mary Beth Oliver, Pennsylvania State University
Hiroshi Ono, Hitotsubashi University
Nate Persily, Stanford University
Barbara Pfetsch, Freie Universität Berlin
Dhavan Shah, University of Wisconsin at Madison
Talia Stroud, University of Texas at Austin
Sharon Strover, University of Texas at Austin
Josh Tucker, New York University
Patti Valkenburg, University of Amsterdam
Claes de Vreese, University of Amsterdam
Ethan Zuckerman, University of Massachusetts at Amherst

Editorial Board

Pablo Barberá, University of Southern California
Jim Bisbee, New York University
Leticia Bode, Georgetown University
Shelley Boulianne, MacEwan University
Moritz Büchi, University of Zurich
Ceren Budak, University of Michigan
Andreu Casas, University of Amsterdam
Teresa Correa, Universidad Diego Portales
Emese Domahidi, TU Ilmenau
Gregory Eady, University of Copenhagen
Deen Freelon, University of North Carolina at Chapel Hill
Sarah Geber, University of Zurich
Amy Gonzales, University of California at Santa Barbara
Sandra González-Bailón, University of Pennsylvania
Darja Groselj, University of Ljubljana
Mako Hill, University of Washington
Matt Hindman, Georgetown University
Matthias Hofer, University of Zurich
Ágnes Horvát, Northwestern University
Kokil Jaidka, National University of Singapore
Dave Karpf, George Washington University
Aaron Kaufman, New York University Abu Dhabi
Eunji Kim, Vanderbilt University
Anders Olof Larsson, Kristiania University College
Edmund Lee, Nanyang Technological University
Yphtach Lelkes, University of Pennsylvania
Chris Lucas, Washington University in St. Louis
Minh Hao Nguyen, University of Zurich
Katherine Ognyanova, Rutgers University
Jen Pan, Stanford University
Cornelius Puschmann, University of Bremen
Elissa Redmiles, Max Planck Institute
Patricia Rossini, University of Liverpool
Bibi Reisdorf, University of North Carolina at Charlotte
Hyunjin Seo, University of Kansas
Aaron Shaw, Northwestern University
Alex Siegel, University of Colorado at Boulder
Tara Slough, New York University
Sarah Shugars, New York University
Emily Thorson, Syracuse University
Michelle Torres, Rice University
Mateo Vasquez, Instituto Tecnologico Autonomo de México
Sebastián Valenzuela, Pontifica Universidad Catolica de Chile
Alessandro Vecchiato, Stanford University
Emily Vraga, University of Minnesota
Brooke Foucault Welles, Northeastern University
Jing Meg Zeng, University of Zurich

Letters of Inquiry

We are currently accepting Letters of Inquiry for potential submissions. We are hoping for a wide range of such inquiries to enable us to establish an appropriate scope for the journal. We will respond to the LOIs submitted at this stage in January. Until our journal platform launches, we are accepting these at the email address below. LOIs should be no longer than a paragraph and address the following:

  • What is being described?
  • How is the sample constructed?
  • How does it pertain to digital media?

Questions? Find us at and on Twitter @journalqd.