BY Emily Hencken Ritter
Earl, Maher, and Pan (2022) present a fascinating synthesis of existing knowledge of digital repression across scholarly disciplines. The typology they apply and extend to frame digital repression highlights who uses digital repression and how it depresses and structures mobilization and dissent actions. In so doing, they center digital repression on existing understandings of how repression attempts to constrain dissent and illuminate what repression studies do not yet know about digital repression and how it functions.
One next step that scholars can take to further understand digital repression is to study how the different actors who engage in these tactics strategically interact with each other and with dissidents to predict and explain dissent outcomes. Scholars of communications, language, data science, and other disciplines will better predict and explain digital repression if they have clearer expectations of unobservable repression.
More recent scholarship on the repression-dissent nexus has made significant strides in understanding the strategic interaction of the state and dissent actors—how mutual anticipation of one another’s choices informs what happens in practice (Pierskalla 2010; Shadmehr and Bernhardt 2011; Ritter 2014; Lehmann and Tyson 2022). Both the state and the dissidents think ahead, considering the likelihood that the other will respond to their activities with damaging, costly, or dangerous consequences. Many dissidents will self-censor to avoid such outcomes, leaving only the more resolved, strong, or stalwart dissidents among those who will act (Ritter 2014). Governments will similarly anticipate dissent actions and use preventive measures to avoid more costly outcomes (Danneman and Ritter 2014). These forward-looking behaviors imply that repressive effects on dissent actions (and vice versa) may be happening but lead to the absence of either or both outcomes in practice—and in data.
In many cases, dissidents take action not to disrupt but instead to inform governments of grievances (Lohmann 1993; Gause 2022). Concessions to address those grievances may be costly, leading the government to repress to shut down the demands (Klein and Regan 2018). In contrast, accommodating narrow demands can benefit the government and avoid future costs, leading governments—including China—to allow and encourage some types of dissent without repressing them (O’Brien and Li 2006; Lorentzen 2013). Identifying the function of the dissent action helps to predict whether the government will respond with repression (because it is threatening disruption) or accommodation (because the demand is narrow or too costly to repress).
Considering these assumptions together—that the actors are strategic and purposive, and dissent can be disruptive or informative—in the context of digital repression implies some difficulty of observation and measurement that social and data scientists should bear in mind in their research (see Ritter and Conrad 2016 for more on the implications for observational studies). Internet shutdowns will prevent dissent, leading to its absence. But a live internet does not necessarily predict dissent activity or activist communication if the dissidents anticipate censorship or more direct repression from authorities. Many dissidents will convey information or grievance in other ways while others will self-censor entirely.
Zero observable repression does not equate to the absence of repression. Data scientists will greatly benefit from a theory informing their models of the data-generating process that accounts for strategic expectations and “missing” data.
Another way in which Earl, Maher, and Pan (2022) innovate in their synthesis is by reminding scholars of repression that private actors are a part of the process of repressing dissent. They are right to point to the dearth of knowledge about how private actors repress to prevent dissent, both in general and especially in digital repression. Political science researchers tend to focus on actors in the public, political arena as the primary agents of repression—national and local government authorities. But private actors prevent dissent as well, via community rules for shared social media and civilian shaming or harassment practices. In general, social scientists have a long way to go to understand private repressors.
As in the research on strategic interaction I describe above, a particular area for future study is in the collaborative process by which state actors encourage or facilitate private actors to restrict speech and activism, channel behavior away from protest, or surveil activity for government action. In a sense, this is a principal-agent relationship, where state actors explicitly or implicitly delegate repressive tasks to companies, organizations, or private citizens. So how does this process work? How do state actors collaborate with and delegate to private actors as a repressive cohort or repressive network? How do they coordinate?
Further, how would we know repression when it is carried out by private actors, and how can we identify state participation in such actions? This type of interaction is another way in which data is likely to be misleading. Complex data modeling may be able to account for this process and connect state policies or actions to coordinated private repression. Again, a clear theory that captures the data-generating process of connected intents and activities will help identify behaviors as collaborative. And here is where digital activities may help scholars to better understand private repression: in-person, private repression is often just that—private—but digital behaviors are available for study. It may be one of the best opportunities yet to understand the process of repression, and thereby undermine it.
References
Danneman, Nathan, and Emily Hencken Ritter. 2014. “Contagious Rebellion and Preemptive Repression.” Journal of Conflict Resolution 58 (2): 254–79.
Earl, Jennifer. 2003. “Tanks, Tear Gas, and Taxes: Toward a Theory of Movement Repression.” Sociological Theory 21 (1): 44–68.
Earl, Jennifer, Thomas V. Maher, and Jennifer Pan. 2022. “The Digital Repression of Social Movements, Protest, and Activism: A Synthetic Review.” Science Advances 8 (10): 1–15.
Gause, LaGina. 2022. “Revealing Issue Salience via Costly Protest: How Legislative Behavior Following Protest Advantages Low-Resource Groups.” British Journal of Political Science 52 (1): 259–79.
Klein, Graig R., and Patrick M. Regan. 2018. “Dynamics of Political Protests.” International Organization 72 (2): 485–521. https://doi.org/10.1017/S0020818318000061.
Lehmann, Todd C., and Scott A. Tyson. 2022. “Sowing the Seeds: Radicalization as a Political Tool.” American Journal of Political Science 66 (2): 485–500. https://doi.org/10.1111/ajps.12602.
Lohmann, Suzanne. 1993. “A Signaling Model of Informative and Manipulative Political Action.” American Political Science Review 87 (2): 319–33.
Lorentzen, Peter. 2013. “Regularizing Rioting: Permitting Public Protest in an Authoritarian Regime.” Quarterly Journal of Political Science 8 (2): 127–58.
O’Brien, Kevin J., and Lianjiang Li. 2006. Rightful Resistance in Rural China. Cambridge University Press.
Pierskalla, Jan Henryk. 2010. “Protest, Deterrence, and Escalation: The Strategic Calculus of Government Repression.” Journal of Conflict Resolution 54 (1): 117–45. https://doi.org/10.1177/0022002709352462.
Ritter, Emily Hencken. 2014. “Policy Disputes, Political Survival, and the Onset and Severity of State Repression.” Journal of Conflict Resolution 58 (1): 143–68.
Ritter, Emily Hencken, and Courtenay R. Conrad. 2016. “Preventing and Responding to Dissent: The Observational Challenges of Explaining Strategic Repression.” American Political Science Review 110 (1): 85–99.
Shadmehr, Mehdi, and Dan Bernhardt. 2011. “Collective Action with Uncertain Payoffs: Coordination, Public Signals, and Punishment Dilemmas.” American Political Science Review 105 (4): 829–51.
Mobilizing Ideas 