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The Diffusion and Reach of (Mis)Information on Facebook During the U.S. 2020 Election

By on December 11, 2024 in Articles

Sandra González-Bailón, David Lazer, Pablo Barberá, William Godel, Hunt Allcott, Taylor Brown, Adriana Crespo-Tenorio, Deen Freelon, Matthew Gentzkow, Andrew M. Guess, Shanto Iyengar, Young Mie Kim, Neil Malhotra, Devra Moehler, Brendan Nyhan, Jennifer Pan, Carlos Velasco Rivera, Jaime Settle, Emily Thorson, Rebekah Tromble, Arjun Wilkins, Magdalena Wojcieszak, Chad Kiewiet de Jonge, Annie Franco, Winter Mason, Natalie Jomini Stroud, Joshua A. Tucker

Sociological Science December 11, 2024
10.15195/v11.a41

Abstract

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Social media creates the possibility for rapid, viral spread of content, but how many posts actually reach millions? And is misinformation special in how it propagates? We answer these questions by analyzing the virality of and exposure to information on Facebook during the U.S. 2020 presidential election. We examine the diffusion trees of the approximately 1 B posts that were re-shared at least once by U.S.-based adults from July 1, 2020, to February 1, 2021. We differentiate misinformation from non-misinformation posts to show that (1) misinformation diffused more slowly, relying on a small number of active users that spread misinformation via long chains of peer-to-peer diffusion that reached millions; non-misinformation spread primarily through one-to-many affordances (mainly, Pages); (2) the relative importance of peer-to-peer spread for misinformation was likely due to an enforcement gap in content moderation policies designed to target mostly Pages and Groups; and (3) periods of aggressive content moderation proximate to the election coincide with dramatic drops in the spread and reach of misinformation and (to a lesser extent) political content.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Sandra González-Bailón: lead author with control rights; Annenberg School for Communication, University of Pennsylvania
E-mail: sandra.gonzalez.bailon@asc.upenn.edu

David Lazer: lead author with control rights; Network Science Institute, Northeastern University
E-mail: d.lazer@northeastern.edu

Pablo Barberá: lead Meta author; Meta
E-mail: us2020research@meta.com

William Godel: lead Meta author; Meta
E-mail: us2020research@meta.com

Hunt Allcott: Environmental and Energy Policy Analysis Center, Stanford University
E-mail: allcott@stanford.edu

Taylor Brown: Meta
E-mail: us2020research@meta.com

Adriana Crespo-Tenorio: Meta
E-mail: us2020research@meta.com

Deen Freelon: Annenberg School for Communication, University of Pennsylvania
E-mail: dfreelon@upenn.edu

Matthew Gentzkow: Department of Economics, Stanford University
E-mail: gentzkow@stanford.edu

Andrew M. Guess: Department of Politics and School of Public and International Affairs, Princeton University
E-mail: aguess@princeton.edu

Shanto Iyengar: Department of Political Science, Stanford University
E-mail: siyengar@stanford.edu

Young Mie Kim: School of Journalism and Mass Communication, University of Wisconsin-Madison
E-mail: ymkim5@wisc.edu

Neil Malhotra: Graduate School of Business, Stanford University
E-mail: neilm@stanford.edu

Devra Moehler: Meta
E-mail: us2020research@meta.com

Brendan Nyhan: Department of Government, Dartmouth College
E-mail: nyhan@dartmouth.edu

Jennifer Pan: Department of Communication, Stanford University
E-mail: jp1@stanford.edu

Carlos Velasco Rivera: Meta
E-mail: us2020research@meta.com

Jaime Settle: Department of Government, William & Mary
E-mail: jsettle@wm.edu

Emily Thorson: Department of Political Science, Syracuse University
E-mail: ethorson@gmail.com

Rebekah Tromble: School of Media and Public Affairs and Institute for Data, Democracy, and Politics, The George Washington University
E-mail: rtromble@email.gwu.edu

Arjun Wilkins: Meta
E-mail: us2020research@meta.com

Magdalena Wojcieszak: Department of Communication, University of California, Davis Center for Excellence in Social Science, University of Warsaw
E-mail: mwojcieszak@ucdavis.edu

Chad Kiewiet de Jonge: Meta research lead; Meta
E-mail: us2020research@meta.com

Annie Franco: Meta research lead; Meta
E-mail: us2020research@meta.com

Winter Mason: Meta research lead; Meta
E-mail: us2020research@meta.com

Natalie Jomini Stroud: co-last author and academic research lead; Moody College of Communication and Center for Media Engagement, University of Texas at Austin
E-mail: tstroud@austin.utexas.edu

Joshua A. Tucker: co-last author and academic research lead; Wilf Family Department of Politics and Center for Social Media and Politics, New York University
E-mail: joshua.tucker@nyu.edu

Acknowledgements: The Facebook Open Research and Transparency (FORT) team provided substantial support in executing the overall project. We are grateful for support on various aspects of project management from Chaya Nayak, Sadaf Zahedi, Lama Ahmad, Akshay Bhalla, Clarice Chan, Andrew Gruen, Bennet Hillenbrand, Pamela McLeod, and Dáire Rice; engineering and research management from Da Li and Itamar Rosenn; engineering from Yuxi Chen, Shiyang Chen, Tegan Lohman, Robert Pyke, and Yixin Wan; data engineering from Suchi Chintha, John Cronin, Devanshu Desai, Vikas Janardhanan, Yann Kiraly, Xinyi Liu, Anastasiia Molchanov, Sandesh Pellakuru, Akshay Tiwari, Chen Xie, and Beixian Xiong; data science and research from Hannah Connolly-Sporing; academic partnerships from Rachel Mersey, Michael Zoorob, Lauren Harrison, Simone Aisiks, Yair Rubinstein, and Cindy Qiao; privacy and legal assessment from Kamila Benzina, Frank Fatigato, John Hassett, Subodh Iyengar, Payman Mohassel, Ali Muzaffar, Ananth Raghunathan and Annie Sun; and content design from Caroline Bernard, Jeanne Breneman, Denise Leto, and Melanie Jennings. NORC at the University of Chicago partnered with Meta on this project to conduct the fieldwork with the survey participants. We are particularly grateful for the partnership of NORC Principal Investigator J.M. Dennis and NORC Project Director Margrethe Montgomery.

Supplemental Materials

Reproducibility Package: Deidentified data and analysis code from this study are deposited in the Social Media Archive at ICPSR, part of the University of Michigan Institute for Social Research. The data are available for university IRB-approved research on elections or to validate the findings of this study. ICPSR will receive and vet all applications for data access. Access through the ICPSR Archive ensures that the data and code are used only for the purposes for which they were created and collected. The code would also be more difficult to navigate separately from the data, which is why both are housed in the same space. Website: https://socialmediaarchive.org/collection/US2020.

  • Citation: González-Bailón, Sandra, David Lazer, Pablo Barberá et al. 2024. “The Diffusion and Reach of (Mis)Information on Facebook During the U.S. 2020 Election.” Sociological Science 11: 1124-1146.
  • Received: September 9, 2024
  • Accepted: October 24, 2024
  • Editors: Arnout van de Rijt, Cristobal Young
  • DOI: 10.15195/v11.a41


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When Do Haters Act? Peer Evaluation, Negative Relationships, and Brokerage

By on April 17, 2024 in Articles

Jason Greenberg, Christopher C. Liu, Leanne ten Brinke

Sociological Science April 17, 2024
10.15195/v11.a16

Abstract

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In many organizational settings, individuals make evaluations in the context of affect-based negative relationships, in which an evaluator personally dislikes the evaluated individual. However, these dislikes are often held in check by norms of professionalism that preclude the use of personal preferences in objective evaluations. In this article, we draw from social network theory to suggest that only individuals that are network brokers—those who have the cognitive freedom to flout organizational norms—act to down-evaluate the peers they dislike. We evaluate our theory using two complementary studies: one field site study and an experiment. Our results, consistent across two different methodologies, suggest that overlooking an evaluator’s negative relationships as well as the network positions that constrain or enable an individual’s actions may lead to distortions in ubiquitous organizational peer evaluations processes and outcomes.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Jason Greenberg: SC Johnson College of Business, Cornell University
E-mail: Jg2459@cornell.edu

Christopher C. Liu: Lundquist College of Business, University of Oregon
E-mail: chrisliu@uoregon.edu

Leanne ten Brinke: Department of Psychology, The University of British Columbia
E-mail: Leanne.tenbrinke@ubc.ca

Acknowledgements: We thank Anne Bowers, Gino Cattani, Sheen Levine, Andras Tilcsik, Catherine Turco, Ezra Zuckerman, and seminar participants at Harvard and NYU for useful feedback on an earlier draft. All errors and omissions are ours alone. This study was IRB approved: (a) NYU IRB HS#10-8124 and (b) Oregon IRB STUDY00001144.

Supplemental Material

Replication Package: Our experiment was preregistered at (https://aspredicted.org/YHD_W9P). A replication package has been deposited at (https://doi.org/10.7910/DVN/4MOJVQ).

  • Citation: Greenberg, Jason, Christopher C. Liu, and Leanne ten Brinke. 2024. “When Do Haters Act? Peer Evaluation, Negative Relationships, and Brokerage.” Sociological Science 11: 439-466.
  • Received: October 20, 2023
  • Accepted: December 23, 2023
  • Editors: Arnout van de Rijt, Stephen Vaisey
  • DOI: 10.15195/v11.a16


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Estimating Homophily in Social Networks Using Dyadic Predictions

By on August 2, 2021 in Articles

George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy

Sociological Science August 2, 2021
10.15195/v8.a14

Abstract

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Predictions of node categories are commonly used to estimate homophily and other relational properties in networks. However, little is known about the validity of using predictions for this task. We show that estimating homophily in a network is a problem of predicting categories of dyads (edges) in the graph. Homophily estimates are unbiased when predictions of dyad categories are unbiased. Node-level prediction models, such as the use of names to classify ethnicity or gender, do not generally produce unbiased predictions of dyad categories and therefore produce biased homophily estimates. Bias comes from three sources: sampling bias, correlation between model errors and node degree, and correlation between node-level model errors along dyads. We examine three methods for estimating homophily: predicting node categories, predicting dyad categories, and a hybrid “ego–alter” approach. This analysis indicates that only the dyadic prediction approach is unbiased, whereas the node-level approach produces both high bias and high overall error. We find that node-level classification performance is not a reliable indicator of accuracy for homophily. Although this article focuses on a particular version of homophily, results generalize to heterophilous cases and other dyadic measures. We conclude with suggestions for research design. Code for this article is available at https://github.com/georgeberry/autocorr.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

George Berry: Department of Sociology, Cornell University
E-mail: geb97@cornell.edu

Antonio Sirianni: Department of Sociology, Dartmouth College
E-mail: antonio.d.sirianni@dartmouth.edu

Ingmar Weber: Qatar Computing Research Institute
E-mail: iweber@hbku.edu.qa

Jisun An: School of Computer and Information Systems, Singapore Management University
E-mail: jisun.an@acm.org

Michael Macy: Department of Sociology, Cornell University
E-mail: mwm14@cornell.edu

Acknowledgments: We thank Thomas Davidson, Mario Molina, Pablo Barberá, Christopher Cameron, Rebecca A. Johnson, Benjamin Cornwell, and Steven Strogatz; participants in the 2020 American Sociological Association section on Mathematical Sociology; the members of the Cornell Social Dynamics Lab; and the members of the Dartmouth Junior Faculty Writing Group for helpful comments and discussions.

Supplemental Material

  • Citation: Berry, George, Antonio Sirianni, Ingmar Weber, Jisun An, and Michael Macy. 2021. “Estimating Homophily in Social Networks Using Dyadic Predictions.” Sociological Science 8: 285-307.
  • Received: January 24, 2021
  • Accepted: April 4, 2021
  • Editors: Jesper Sørensen, Filiz Garip
  • DOI: 10.15195/v8.a14


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Neighborhood Isolation during the COVID-19 Pandemic

By on June 14, 2021 in Articles

Thomas Marlow, Kinga Makovi, Bruno Abrahao

Sociological Science June 14, 2021
10.15195/v8.a9

Abstract

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The COVID-19 pandemic has disrupted Americans’ daily mobility, which could contribute to greater social stratification. Relying on SafeGraph cell phone movement data from 2019 and 2020, we use two indices proposed by Phillips and colleagues (2019) to measure mobility inequality between census tracts in the 25 largest U.S. cities. These measures capture the importance of hubs and neighborhood isolation in a network. In the earliest phases of the pandemic, neighborhood isolation rapidly increased, and the importance of downtown central business districts declined. Mobility hubs generally regained their importance, whereas neighborhood isolation remained elevated and increased again during the latter half of 2020. Linear regression models with city and week fixed effects find that new COVID-19 cases are positively associated with neighborhood isolation changes a week later. Additionally, places with larger populations, more public transportation use, and greater racial and ethnic segregation had larger increases in neighborhood isolation during 2020.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Thomas Marlow: Center for Interacting Urban Networks (CITIES), New York University Abu Dhabi
E-mail: twm9710@nyu.edu

Kinga Makovi: Social Science Division, New York University Abu Dhabi
E-mail: km2537@nyu.edu

Bruno Abrahao: Business Division, New York University Shanghai; Global Network Assistant Professor, Leonard N. Stern School of Business, New York University
E-mail: bd58@nyu.edu

Acknowledgments: We would like to thank Byungkyu Lee, Philipp Brandt, and Clara G. Sears for their insightful feedback on earlier drafts. This work was supported by the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award CG001 and by the Swiss Re Institute under the Quantum Cities initiative. Bruno Abrahao was supported by National Natural Science Foundation of China (NSFC) Grant 61850410536.

Supplemental Material

  • Citation: Marlow, Thomas, Kinga Makovi, and Bruno Abrahao. 2021. “Neighborhood Isolation during the COVID-19 Pandemic.” Sociological Science 8: 170-190.
  • Received: March 14, 2021
  • Accepted: April 22, 2021
  • Editors: Jesper Sørensen, Mario Small
  • DOI: 10.15195/v8.a9


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Generalized Markovian Quantity Distribution Systems: Social Science Applications

By on October 8, 2020 in Articles

Noah E. Friedkin, Anton V. Proskurnikov

Sociological Science October 8, 2020
10.15195/v7.a20

Abstract
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We propose a model of Markovian quantity flows on connected networks that relaxes several properties of the standard compartmental Markov process. The motivation of our generalization are social science applications of the standard model that do not comport with its steady state predictions. The proposed generalization relaxes the predictions that every node belonging to the same nontrivial strong component of a network must acquire the same fraction of its members’ initial quantities and that the sink component(s) of the network must absorb all of the system’s available initial quantity. For example, when applied to refugee flows from a nation in chaos to other nations on a network with one or more sink nations, the standard model predicts that all the refugees will be eventually located in the sink(s) of the network and none that will permanently locate themselves in the nations along the paths to the sink(s). We illustrate this and several other social science applications to which our proposed model is applicable.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Noah E. Friedkin: Department of Sociology and the Center for Control, Dynamical Systems, and Computation, University of California, Santa Barbara
E-mail: friedkin@soc.ucsb.edu

Anton V. Proskurnikov: Politecnico di Torino, Turin, Italy, and the Institute for Problems of Mechanical Engineering of the Russian Academy of Sciences, St. Petersburg, Russia
E-mail: anton.p.1982@ieee.org

  • Citation: Friedkin, Noah E., and Anton V. Proskurnikov. 2020. “Generalized Markovian Quantity Distribution Systems: Social Science Applications.” Sociological Science 7: 487-503.
  • Received: September 3, 2020
  • Accepted: September 10, 2020
  • Editors: Jesper Sørensen, Olav Sorenson
  • DOI: 10.15195/v7.a20


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Making Friends in Violent Neighborhoods: Strategies among Elementary School Children

By on March 15, 2017 in Articles

Anjanette M. Chan Tack, Mario L. Small

Sociological Science, March 15, 2017
DOI 10.15195/v4.a10

Abstract
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While many studies have examined friendship formation among children in conventional contexts, comparatively fewer have examined how the process is shaped by neighborhood violence. The literature on violence and gangs has identified coping strategies that likely affect friendships, but most children in violent neighborhoods are not gang members, and not all friendship relations involve gangs. We examine the friendship-formation process based on in-depth interviews with 72 students, parents, and teachers in two elementary schools in violent Chicago neighborhoods. All students were African American boys and girls ages 11 to 15. We find that while conventional studies depict friendship formation among children as largely affective in nature, the process among the students we observed was, instead, primarily strategic. The children’s strategies were not singular but heterogeneous and malleable in nature. We identify and document five distinct strategies: protection seeking, avoidance, testing, cultivating questioners, and kin reliance. Girls were as affected as boys were, and they also reported additional preoccupations associated with sexual violence. We discuss implications for theories of friendship formation, violence, and neighborhood effects.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Anjanette M. Chan Tack: Department of Sociology, University of Chicago
Email: amc75@uchicago.edu

Mario L. Small: Department of Sociology, Harvard University
Email: mariosmall@fas.harvard.edu

Acknowledgements: This research was supported by the MacArthur Foundation, the University of Chicago, the National Opinion Research Center, and Harvard University. We thank Karen Davis and Lara Perez-Felkner for fieldwork, interview work, and other research assistance instrumental to this project and David Harding for comments and criticisms. Direct correspondence to Mario L. Small, 33 Kirkland St, Department of Sociology, Cambridge, MA 02138 or mariosmall@fas.harvard.edu.

  • Citation: Chan Tack, Anjanette M., and Mario L. Small. 2017. “Making Friends in Violent Neighborhoods: Strategies among Elementary School Children.” Sociological Science 4: 224-248.
  • Received: January 12, 2017
  • Accepted: February 8, 2017
  • Editors: Jesper B. Sørensen, Gabriel Rossman
  • DOI: 10.15195/v4.a10


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Consensus, Polarization, and Alignment in the Economics Profession

By on December 5, 2016 in Articles

Tod S. Van Gunten, John Levi Martin, Misha Teplitskiy

Sociological Science, December 5, 2016
DOI 10.15195/v3.a45

Abstract
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Scholars interested in the political influence of the economics profession debate whether the discipline is unified by policy consensus or divided among competing schools or factions. We address this question by reanalyzing a unique recent survey of elite economists. We present a theoretical framework based on a formal sociological approach to the structure of belief systems and propose alignment, rather than consensus or polarization, as a model for the structure of belief in the economics profession. Moreover, we argue that social clustering in a heterogeneous network topology is a better model for disciplinary social structure than discrete factionalization. Results show that there is a robust latent ideological dimension related to economists’ departmental affiliations and political partisanship. Furthermore, we show that economists closer to one another in informal social networks also share more similar ideologies.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Tod S. Van Gunten: Max Planck Institute for the Study of Societies
Email: tvg@mpifg.de

John Levi Martin: Department of Sociology, University of Chicago
Email: jlmartin@uchicago.edu

Misha Teplitskiy: Institute for Quantitative Social Science, Harvard University
Email: mteplitskiy@fas.harvard.edu

Acknowledgements: The authors would like to thank Anil Kashyap, Brian Barry, and the Initiative on Global Markets at the Booth School of Business of the University of Chicago for providing data access.


  • Citation: Van Gunten, Tod S., John Levi Martin, and Misha Teplitskiy. 2016. “Consensus, Polarization, and Alignment in the Economics Profession.” Sociological Science 3: 1028-1052.
  • Received: October 8, 2016
  • Accepted: October 26, 2016
  • Editors: Jesper Sørensen, Gabriel Rossman
  • DOI: 10.15195/v3.a45


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The Strength of Weak Ties in MBA Job Search: A Within–Person Test

By on May 18, 2016 in Articles

Jason Greenberg, Roberto M. Fernandez

Sociological Science, May 18, 2016
DOI 10.15195/v3.a14

Abstract
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Whether and how social ties create value has inspired substantial research in organizational theory, sociology, and economics. Scholars generally believe that social ties impact labor market outcomes. Two explanatory mechanisms have been identified, emphasizing access to better job offers in pecuniary terms and the efficacy of non-redundant information. The evidence informing each theory, however, has been inconsistent and circumstantial. We test predictions from both models using a rich set of job search data collected from an MBA student population, including detailed information about search channels and characteristics of job offers. Importantly, we can compare offers made to the same student derived via different search channels while accounting for industry, function, and non-pecuniary characteristics. We find that contrary to conventional wisdom, search through social networks typically results in job offers with lower total compensation (-17 percent for referrals through strong ties and -16 percent for referrals via weak ties vs. formal search). However, our models also show that students are considerably more likely to accept offers derived via weak ties. They do so because they are perceived to have greater growth potential and other non-pecuniary value. On balance, our tests are consistent with Granovetter’s argument that networks provide value by facilitating access to information that is otherwise difficult to obtain, rather than providing greater pecuniary compensation.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Jason Greenberg: Leonard N. Stern School of Business, New York University
Email: jgreenbe@stern.nyu.edu

Roberto M. Fernandez: MIT Sloan School of Management, Massachusetts Institute of Technology
Email: robertof@mit.edu

Acknowledgements: This paper was presented in a symposium at the annual American Sociological Association meeting honoring the fortieth anniversary of Mark Granovetter’s classic Getting a Job.We thank the organizing members of that symposium (Nina Bandelj and Emilio Castilla), co-panelists, and audience members for useful feedback. Thanks are also due audiences at Michigan-ICOS and NYU, Gino Cattani, and Mark Granovetter. All the usual disclaimers apply. Please send questions or comments to Jason Greenberg (jgreenbe@stern.nyu.edu)

Supplemental Material

  • Citation: Jason Greenberg and Roberto M. Fernandez.  2016.“The Strength of Weak Ties in MBA Job Search:  A Within–Person Test.” Sociological Science 3: 296-316
  • Received: January 4, 2016
  • Accepted: January 27, 2016
  • Editors: Jesper Sørensen, Olav Sorenson
  • DOI: 10.15195/v3.a14

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Extending the INGO Network Country Score 1950-2008

By on May 20, 2015 in Articles

Pamela Paxton, Melanie M. Hughes, Nicholas E. Reith

Sociological Science, May 20, 2015
DOI 10.15195/v2.a14

Abstract
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Hughes et al. (2009) introduced the INGO Network Country Score (INCS), a measure of country-level connectedness to the world polity, for three years: 1978, 1988, and 1998. The measure scores countries by centrality in the world country-INGO network, rather than on raw counts of INGO ties that do not acknowledge networks or power. In this article, we extend the measure by time, space, organization, and calculation. First, we extend the measure to the period 1950âAS2008, allowing closer correspondence to the years typically assessed by researchers. Second, we extend the country samples upon which the scores are based, allowing researchers greater flexibility in choosing samples. Third, we extend the number of INGOs from which the scores are created. The Hughes et al. (2009) INCS were based on a single-year maximum of 476 INGOs; ours are based on a single-year maximum of 1,604 INGOs (5,291 INGOs across all years). Finally, we provide both raw and scaled scores, which we use to discuss the observed increasing density in the world polity from 1950 to 2008, comparing scores across regions. Results reveal higher average INCS with less variability among Western countries, and significant inequality between the West and the rest of the world.
 
Pamela Paxton: Department of Sociology, The University of Texas at Austin.  Email: ppaxton@prc.utexas.edu

Melanie M. Hughes: Department of Sociology, University of Pittsburgh. Email: hughesm@pitt.edu

Nicholas E. Reith: Department of Sociology, The University of Texas at Austin.  Email:nreith@utexas.edu

Acknowledgements: We gratefully acknowledge the support of the National Science Foundation SES-1067218 and SES-1323130.

Supplemental Material

  • Citation: Paxton, Pamela, Melanie M. Hughes, and Nicholas E. Reith. 2015. “Extending the INGO Network Country Score, 1950–2008” Sociological Science 2: 287-307.
  • Received: July 15, 2014
  • Accepted: November 26, 2014
  • Editors: Jesper Sorensen, Sarah Soule
  • DOI: 10.15195/v2.a14

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