I conduct an experiment which examines the impact of group norm promotion and social sanctioning on racist online harassment. Racist online harassment de-mobilizes the minorities it targets, and the open, unopposed expression of racism in a public forum can legitimize racist viewpoints and prime ethnocentrism. I employ an intervention designed to reduce the use of anti-black racist slurs by white men on Twitter. I collect a sample of Twitter users who have harassed other users and use accounts I control (“bots”) to sanction the harassers. By varying the identity of the bots between in-group (white man) and out-group (black man) and by varying the number of Twitter followers each bot has, I find that subjects who were sanctioned by a high-follower white male significantly reduced their use of a racist slur. This paper extends findings from lab experiments to a naturalistic setting using an objective, behavioral outcome measure and a continuous 2-month data collection period. This represents an advance in the study of prejudiced behavior.
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All hypotheses were pre-registered at EGAP.org (ID number 20150520AA) prior to any data collection.
Whether a picture is actually of the subject was impossible to verify perfectly; I included any picture that clearly showed the face of a person who I did not recognize.
As is recorded in my pre-analyis plan (registered at EGAP, ID number 20150520AA), I had originally intended to perform two similar experiments: one on racist harassment, and the other on misogynist harassment. However, my method was insufficient for generating a large enough sample of misogynist users. For any misogynist slur I tried to use as my search term (bitch, whore, slut), there were far too many people using it as a term of endearment for their friends for me to filter through and find the actual harassment. I plan on figuring out a way to crowdsource this process of manually discerning genuine harassment, but for now, the misogynist harassment experiment is unfeasible. The pre-analysis plan also intended to test two hypotheses about spillover effects on the subjects’ networks, but this has thus far proven technically intractable.
For a full list of terms, see the Online Appendix.
Each Twitter account is assigned a unique numerical user ID based on when they signed up; newer accounts have higher ID’s. Not all of the numbers correspond to extant or frequently used accounts, so if I randomly picked one of those numbers, I generated a new random number.
Still, there are many people who believe that they’re “joking” when they call a friend a slur. While this is still objectionable behavior, it is different from the kind of targeted prejudiced harassment that is of interest in this paper, so I excluded from the sample any users who appeared to be friends who did not find the slur they were using offensive. This process is inherently subjective, but it usually entailed the users with a long back-and-forth, with slurs interspersed with more obviously friendly terms.
Throughout the assignment process, I matched subjects in each treatment group on their (0–2) anonymity score. They were otherwise randomly assigned.
This process was approved by NYU’s Institutional Review Board. These subjects had not given their informed consent to participate in this experiment, but the intervention I applied falls within the “normal expectations” of their user experience on Twitter. The subjects were not debriefed. The benefits to their debriefing would not outweigh the risks to me, the researcher, in providing my personal information to a group of people with a demonstrated propensity for online harassment.
I avoid providing the entire username of the bot to protect my subjects’ anonymity.
It is possible that a stronger racial treatment effect might have obtained if I also changed the facial features of the black bots to be more afrocentric, the effect of which Weaver (2012) finds to be approximately as large as changing skin color on voting outcomes.
Initially, I assigned 243 subjects to one of the four treatment arms or to the control group. However, the rate of tweeting of one of these subjects was too infrequent for me to be able to calculate a meaningful pre-treatment rate of offensive language use, and I excluded him.
I contacted Twitter to see if they could provide me with this information, but they were not forthcoming.
Note, though, that the Out-group/High Followers condition saw much lower attrition than the other treatment conditions. I have no explanation for why this is the case, and in fact my ex ante expectation was that, to the extent that attrition was positively correlated with any treatment condition, it would have been higher among the High Followers conditions.
A more conservative and less substantively accurate assumption is to treat these observations as having a post-treatment rate of racist language equal to their pre-treatment rate of racist language use. Figure 7 in the Appendix presents the results with this alternate assumption. The results are substantively similar, although the point estimates are slightly smaller.
I have selected my sample based on their use of this slur. Expanding the dependent variable to include other anti-black language does not substantively change the results, primarily because the use of other anti-black slurs is uncommon among this subject pool.
These responses also did not vary in terms of vitriol between the treatment arms. In fact, even the number of subjects that responded to call my bot a “n****r” did not vary significantly between the white and black bots.
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I would like to thank Chris Dawes, Neal Beck, Eric Dickson, James Hodgdon Bisbee, David Broockman, Livio Di Lonardo, Ryan Enos and Drew Dimmery, along with three anonymous reviewers; participants at the 2015 Summer Methods Meeting, the Harvard Experimental Political Science Graduate Student Conference, Neal's Seminar, the Yale ISPS Experiments Workshop and the NYU Graduate Political Economy Seminar; and members of the NYU Social Media and Political Participation (SMaPP) Lab, for their valuable feedback on earlier versions of this project.
Conflict of interest
The author declares that he had no conflicts of interest with respect to his authorship or the publication of this article.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the New York University Institutional Review Board.
Replication materials are available on the author’s website, www.kevinmunger.com.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Conservative Assumption for Main Results
For the subjects who produced too few post-treatment tweets to calculate an rate of racist language use, I assumed that their post-treatment rate of racist language use was zero. This assumption makes sense substantively, because these people were no longer tweeting (and thus no longer engaging in racist harassment). However, a more conservative assumption would be to assume that there was no change in their behavior, and to assign them a post-treatment rate equal to the their pre-treatment rate. This does not substantively change the results, although the magnitude of the effect sizes becomes slightly smaller.
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Munger, K. Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment. Polit Behav 39, 629–649 (2017). https://doi.org/10.1007/s11109-016-9373-5
- Online harassment
- Social media
- Randomized field experiment
- Social identity