Internet users tend to navigate between websites in a racially segregated way, despite pathways that provide equitable access to different sites, finds a new NYU Steinhardt study. The findings are published online in the journal Information, Communication, and Society.
Discussions about racial inequality on the web have been going on for decades, but few studies have attempted to demonstrate whether and how systemic racial inequality might form on the web.
“We know that people do racist things on and using the Internet – but looking beyond individual, interpersonal accounts of bigotry, how does systemic racial inequality form in the digital world?” asked Charlton McIlwain, associate professor of media, culture, and communication at NYU Steinhardt and the study’s author.
McIlwain designed a study to conceptualize how race is represented and systematically reproduced online, specifically looking at how users navigate the web’s structure and how that structure influences users’ navigational patterns. He used the lens of racial formation theory, which conceptualizes how institutions draw on prevailing racial common sense to produce advantages and disadvantages that flow to racial groups.
“We must consider how the Internet developed as a part of a longstanding history and process of racial formation – the complex, racialized historical contexts, circumstances, interests, and problems that predate, but may either be exacerbated or corrected by the web’s technological environment,” said McIlwain.
Creating an original dataset, McIlwain documented racial and nonracial websites. Sites were designated racial or nonracial depending on whether race-related terminology was used in the websites’ title, description, or keyword meta-tags. He also gathered data on each site’s ranking based on traffic and other factors.
McIlwain then created the architecture of the actual traffic patterns among and between racial and nonracial sites using a program that employs a spatial algorithm to compare links between sites. The program calculated the expected number of connections within and between racial and nonracial sites based on chance, and then compared whether the actual connections significantly exceed or fall below what was expected. For instance, in what McIlwain defines as a segregated traffic pattern, the links between racial and nonracial sites would be significantly fewer than expected.
McIlwain found that web producers create hyperlink networks that do not steer audience traffic to other sites based on their racial or nonracial nature. However, the opposite pattern emerged when looking at users going to and coming from sites in the network. McIlwain found that user navigation reflects a racially segregated traffic pattern, where visitors to nonracial sites visit other nonracial sites with greater frequency than what would be expected by chance, and visitors to racial sites visit other racial sites more than expected.
“The evidence suggests a tendency toward racially segregated site navigation. Web producers seem to build pathways providing equitable access to sites, without concern for the racial nature of the site. This might produce truly equitable traffic patterns if users only relied on site links to direct the flow of traffic. But other things intervene, including individuals’ own choices, search engines, or a combination of both,” said McIlwain.
“Just because people build a road to get from point A to B does not mean people will choose to drive on it, or use it to go from point A to C, when C is a destination that comports more with their individual preferences.”
The findings demonstrate that variables that have historically contributed to racial inequality offline, such as segregated traffic patterns and destinations, are present within the web’s environment.
“These results, along with disparities in website traffic rankings, show how a race-based hierarchy might systematically emerge on the web in ways that exemplify disparate forms of value, influence, and power that exist within the web environment,” said McIlwain.
This work was supported by IDEAs Challenge Grant from NYU Steinhardt.
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