Hegemony of the Fad: Ephemerality, Reproduction and the Loss of Time for Thought on TikTok


Published

23 January 2022

The task of thinking is laborious; it is time-consuming, sometimes painful and requires focused and protracted attention. Consuming content on social media is easy; it requires passive, frictionless attention with the addictive benefit of a dopamine hit. I thus want to ask:

Has a capitalist logic of the fad, geared towards getting and keeping attention with speed and novelty affected our ability to meaningfully orientate ourselves in the world by reducing the time for thought?

I have used case study analysis of TikTok as a way into this problem as it highlights 1) the urge for newness and speed in the attention economy; 2) How the platform has changed what media literacy means, and 3) the challenges posed to thinking in high-speed society. I have drawn upon the experience of TikTok users and creators to foreground how the app comes to serve as an ontological template for reality and thus how it relates to the process of thinking. I propose the concept of the ‘fad’ to understand and critique TikTok and high-speed society more broadly. A fad can be loosely described as fashions, activities, or interests that are highly popular for short periods.

TikTok, as well as other tech firms, are incredibly good at capitalising on ephemeral trends. Fads are born, burn and bust with staggering speed. The concept of the fad is a way to make sense of the flattening of culture into replicable meme’s and this goes some way to explaining the rapid speed of cultural reproduction we currently see. When TikTok’s algorithm launches a video—say, a funny dance synced with music—over the app, the next day the whole user base could be replicating the dance, with the video having accrued millions of views. Because of the structuring of the ‘for you page’, any video can go viral, even those from accounts with very few followers. TikTok has consciously set the conditions for fads and then crowdsourced the creativity and reaps the financial rewards in terms of user attention. Life in the age of TikTok is the hegemony of the fad. Bite-size, replicable on a vast scale, it is the commodification and mass production of culture as meme.

But an economy of fads is only sustainable in an environment with a superabundance of content. The founder of TikTok, Zhang Yiming realised that if you want a content-based community the barrier to content creation needs to be extremely low. It needs to take minutes or hours to create, not days or weeks, and the equipment needed to create videos needs to be minimal. TikTok learnt from the failures of other video apps. One key thing it did was to ensure that video length was short. The short duration and ephemerality of videos on Tik-Tok are a design trick to generate vast swathes of information. A consequence of this is a change in what media literacy entails. It is no longer about being able to critically interrogate text or video and question sources. What is the point of critical thought when the object of your attention is likely 15-30 seconds long and then disappears from your screen, likely never to be seen again?

Media literacy today means being able to share and interact with content, replicate fads, and if you so choose, try your hand at creating them. This is the broader context one needs to consider when attempting to critically read algorithms of this kind. TikTok’s algorithm has been lauded as an engineering marvel. But there is nothing distinctly unique about it. Its engineers haven’t broken some algorithmic threshold that enables higher levels of precision when predicting user preference. It just comes from vast volumes of data that the short natured videos and fad-based ecosystem provides as well as a user base who are open to consuming algorithmically recommended content.

TikTok has perfected strategies pioneered by other platforms in the attention economy. What it has produced is a product that is so addictive that it fills all space of intermittence, the space of dreaming and imagination, the space of reflective thinking. TikTok doesn’t want you to pause for thought, it wants to feed you a continuous flowing loop of video that will trigger a dopamine hit. Open the app and the video starts playing immediately, swipe up for more content. It takes roughly half an hour of watch time or roughly 250 videos before the algorithm will understand your preferences. It does this principally via the hesitation, lingering or re-watching of a video. The longer spent watching the more weighting the video receives. It will quickly rabbit hole a user with recommendations, pushing them to more extreme content. This relentless capture of user time and experience leads to the loss of the space for contemplation, reflection and even to the experience of boredom.

The case of TikTok is emblematic of the direction of travel in platform capitalism. It shows us the reality of media literacy for the roughly 1 billion monthly active users on the app. The concept of the fad is an insightful tool when trying to make sense of high-speed society as it shows us why short-lived, highly replicable content is so valuable in the information economy. But the state of thoughtless flow this system generates is antithetical to the arduous task of thinking. TikTok is by no means the only culprit. But its case should move us to think about the billions upon billions of lost hours of thinking, reflection and boredom that have been replaced by the lure of dopamine laced passive attention. Finally, my research should encourage us to contextualise algorithms when analysing platforms. This is because the ecosystems that apps create are just as important as the algorithms themselves. And unlike the highly secretive ‘black box’ algorithms, the structures of the platform giants are readily available and open to critique.

Published

23 January 2022


Contributed by

Ben Potter is a PhD candidate in Social and Political Thought at the University of Sussex. His research topic is ‘Meaning, self-understanding and interpretation in the age of machine learning’.

Contributed as part of Symposium: Making Sense of the High-Speed Society