Twitter users—and Twitter as a company—have grappled with ways to deal with hateful, bigoted, and harassing speech throughout the platform's lifetime. The service has added a few tools to try to keep things in check, but one PhD student at NYU's school of politics set out to see whether social checks and balances could reduce the platform's most abhorrent speech.

The results, published in the November edition of Political Behavior, concluded that direct, negative responses to racist tweets could have an impact—but, at least in this test's case, they were far more effective when they appeared to come from white users.

NYU student Kevin Munger began his experiment by identifying 231 Twitter accounts with a propensity for using the n-word in a targeted manner (meaning, the message included the "@" symbol and used second-person language). All of these accounts were at least six months old and had used the n-word in at least three percent of their posts during the period Munger monitored them (late summer last year). Munger explains that he chose white men as the study's subjects "because they are the largest and most politically salient demographic engaging in racist online harassment of blacks," and also to control "the in-groups of interest (gender and race)."

He created fake Twitter accounts to target each of these users with a simple phrase, always in response to an apparently harassing use of the n-word: "@[subject] Hey man, just remember that there are real people who are hurt when you harass them with that kind of language."

Munger's fake accounts were puffed up with varying numbers of followers, which he purchased through a fake follower-bot service, and they were identified by names traditionally associated with white or black people, along with a cartoon avatar of a white or black man to match. Munger wanted to test a few things: whether the admonishing account's race or follower count would draw a different response, and whether a user's anonymity would influence their behavior. (Anonymity was scored based on whether a Twitter account had no real name, photo, or identifying information in either its profile or its posts.)

Munger's data shows that a rebuke from an apparent white user with a high follower count had the most impact, and this impact carried more weight with the most anonymous Twitter users. In these cases, future posts containing the n-word dropped by roughly 27 percent compared to a control group in the following week. That drop-off leveled out somewhat in two-week and one-month follow-ups, but it remained. (As Munger puts it, "the 50 subjects in the most effective treatment condition tweeted the word 'nigger' an estimated 186 fewer times in the month after treatment.")

However, apparent white users with low follower counts, as well as any apparent black users, showed no measurable impact. Non-anonymous users were less convinced as well—and were more likely to post a negative response to the bot. Munger scanned affected users' activity over the same two-month time span to see if "substitution" had occurred in the form of misogynistic language, and he concluded that such a shift did not occur.

The study's scope is certainly limited in that it focuses primarily on anti-black speech, as opposed to other bigoted behavior. Munger notes that the subjects who qualified for this study, "most commonly manifested" as followers of either the GamerGate hashtag or the Donald Trump presidential campaign. These campaigns, he points out, tend to engage in more anti-female and anti-immigrant speech on social media. Munger also doesn't spell out exactly how many fake accounts he created in all. Two of his accounts, GregJackson730 and RasheedSmith45 , remain online, but their tweets have been protected, so we can't check whether those accounts' posts were stacked in such a way that a recipient could click the account and see a glut of the same admonishing reply to other users.

"Although this study’s evidence of a method to reduce the expression of prejudice online is valuable in and of itself, the question remains as to whether this effect changes underlying prejudiced attitudes or behavior in the physical world," Munger writes.

More mutes

Tuesday also saw Twitter announce an upcoming rollout of new "muting" abilities for all users. This will appear on the social network's "notifications" tab and will allow users to automatically block single keywords and complete phrases from appearing there. More importantly for how Twitter works, however, is the "mute conversation" feature, which will let users opt out of sprawling, reply-filled conversation that they never wanted to be a part of in the first place.

The announcement also included a promise that users will soon get "a more direct way to report... hateful conduct" on the site. Whether the reports will be acted on is a separate question, considering that Twitter has already made such announcements repeatedly in the past few years. To the company's credit, Tuesday's announcement acknowledges that a single proclamation isn't the end-all of the process. "We don’t expect these announcements to suddenly remove abusive conduct from Twitter," the company said. "No single action by us would do that. Instead we commit to rapidly improving Twitter based on everything we observe and learn."

Political Behavior, 2016. DOI: 10.1007/s11109-016-9373-5 (About DOIs).

This report has been updated to clarify the study's focus on exclusively white male subjects.