IZZY BLAIR
Inherent Oppression and Bias in the Tiktok Algorithm
With over one billion people using the app every month (a), Tiktok has become one of the world’s most popular social media and video sharing apps. While there are many reasons for the app’s popularity, the most known reason is the app’s astoundingly efficient and controversial algorithm for user video curation. Each user has their own “For You Page”, on which the algorithm chooses videos for you based on your interaction with previous videos. Each user can also allow their data and activity to be tracked across other apps they use, much like Facebook does. TikTok has its own Community Guidelines and that all user videos must follow, as well as “technology that identifies and flags potential policy violations, such as adult nudity and violent and graphic content” (b) to keep these rules in check. However, with this technology comes a lot of biases that users have noticed.
The phenomenon of the “echo chamber” is one that Tiktok users are familiar with. It describes the concept that the algorithm gives each user a video that perpetuates the type of content the user has already expressed interest in. If a user repeatedly likes, comments, and/or shares videos about knitting, they will get even more knitting videos on their For You Page. This is why Tiktok is so popular: it rewards higher levels of activity with more content the user will enjoy, thus creating an echo chamber.
While this is great for users who enjoy knitting, there is a dark side to this echo chamber—one that can perpetuate hateful ideas and videos. One would think that the same technology that flags adult nudity and other visual content violations would also be able to flag these hateful ideologies as dangerous, but there is no such proof of any technology. Of course, Tiktok has human reviewers who go through each flag, but there are still many cases of hateful ideologies not being flagged or punished in any way. One user reported that his profile bio was flagged for using the word “black” in any capacity, whether it was “I am a black man” to “Black Lives Matter” (c). However, the phrases “white supremacy” and “I am a neo-Nazi" did not prompt the same flags for violations. The same algorithm that supposedly brings “new content” into the user’s feed is the same one that narrows down the scope of what a user is actually consuming. In a country as politically, economically, and socially polarized as the United States, the creation of barriers between users with different viewpoints creates tension between a user and anyone who disagrees with them. This brings up an important question: are these biases just simple errors in the algorithm, or was this intentional?