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Moralization in social networks and the emergence of violence during protests

Nature Human Behaviourvolume 2pages389396 (2018) | Download Citation


In recent years, protesters in the United States have clashed violently with police and counter-protesters on numerous occasions1,2,3. Despite widespread media attention, little scientific research has been devoted to understanding this rise in the number of violent protests. We propose that this phenomenon can be understood as a function of an individual’s moralization of a cause and the degree to which they believe others in their social network moralize that cause. Using data from the 2015 Baltimore protests, we show that not only did the degree of moral rhetoric used on social media increase on days with violent protests but also that the hourly frequency of morally relevant tweets predicted the future counts of arrest during protests, suggesting an association between moralization and protest violence. To better understand the structure of this association, we ran a series of controlled behavioural experiments demonstrating that people are more likely to endorse a violent protest for a given issue when they moralize the issue; however, this effect is moderated by the degree to which people believe others share their values. We discuss how online social networks may contribute to inflations of protest violence.

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We thank A. Damasio, D. Medin, S. Atran, J. Kaplan, K. Man, R. Iliev, S. Sachdeva and UCSB’s Psychology, Environment and Public Policy group for their feedback on an earlier draft of this manuscript. This research was sponsored by the Army Research Lab. The content of this publication does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Marlon Mooijman, Joe Hoover.


  1. Department of Management and Organizations, Kellogg School of Management, Northwestern University, Evanston, IL, USA

    • Marlon Mooijman
  2. Brain and Creativity Institute, University of Southern California, Los Angeles, CA, USA

    • Joe Hoover
    •  & Morteza Dehghani
  3. Psychology Department, University of Southern California, Los Angeles, CA, USA

    • Joe Hoover
    •  & Morteza Dehghani
  4. Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA

    • Ying Lin
    •  & Heng Ji
  5. Department of Computer Science, University of Southern California, Los Angeles, CA, USA

    • Morteza Dehghani


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M.M., J.H. and M.D. developed the theory. M.M. designed, ran and analysed studies 2–4. J.H. designed, ran and analysed study 1. V.L. and H.J. designed the text analysis method used to classify tweets. M.M., J.H. and M.D. wrote the paper.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Marlon Mooijman or Joe Hoover or Morteza Dehghani.

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