Cascading Behavior & Diffusion in Networks- TikTok
I’d like to discuss the role of information cascades in trending posts or hashtags in social media, but more specifically on TikTok. TikTok is a social media platform for creating and sharing short videos, similar to the former app known as Vine. I chose this platform because I have noticed a recent cascade of people at Cornell downloading and using the app. The app is commonly described as the “ultimate time killer and very addictive because it never runs out of content” (Middleton). However, I do not want to focus on the cascade of people downloading the app, but rather the way the app is designed to create a domino effect of specific content.
The app has a “For You” page, similar to Instagram’s explore page, which doesn’t show content of the people you follow but instead, displays the content that the app recommends for you. Unlike Instagram, the For You page is the default page, rather than the users you are actually following. This is a way for the app’s developers to instantly feed its users new information and trends, which keeps the app afloat. Trends are the major part of the TikTok experience. After people see videos on the For You page, there is an incentive to recreate the same video, using the same song, and similar hashtags because one will likely receive a better payoff from this than making a completely original video.The algorithm for how TikTok chooses videos for the For You page is not made public, but it is believed that it is based on the trends that a user has liked in the past. Thus, the payoff of recreating a trending video is that you will have a better chance of being on the For You page and gaining more followers. A better idea of the For You page is that many of the same video concepts are shown, but they are done by many different people and possibly made slightly different each time
There are also users that do not contribute videos to the app, but are passive watchers of other users’ videos. There is also a cascade of information among this perspective as well. The cascade does not stop with downloading the app if most of your neighbor nodes, or friends, have the app as well. If a trending TikTok video is shared with you by a friend, it is more likely that you will like a video of the same trend if it pops up on your For You page, whether or not you would have liked the video previously with your personal information. This in turn, would make that trend pop up on your For You page a lot more often. Referring to an example from class, this phenomenon is similar to how people repost certain social movements that their friends post on social media regardless of whether they felt strongly about the movement beforehand. This can be described by the “diffusion of innovations”, which is “the spread of a new technology or idea through a group of people, and analyzing the factors that facilitated or impeded its progress” (Easley and Kleinberg). Diffusion of innovations justifies what causes cascades of video content to spread through entire clusters of nodes, or friend groups, because people with close links tend to start following and liking the same TikTok trends.
Sources:
Easley, David, and Jon Kleinberg. Networks, Crowds, and Markets Reasoning about a Highly Connected World. Cambridge University Press, 2010.
Middleton, Anna. “How Does the Tik Tok Algorithm Work.” Tech Junkie, www.techjunkie.com/how-does-the-tik-tok-algorithm-work/.