Social media has evolved significantly from its early days, where social media was used to share photos with friends, write short updates about your life and follow pages that you like. In todays world behind every post, like and video, there is an algorithm that is deciding which contents you are going to watch, what content is going ‘viral’ and what products will appear in your advertisements.
Nowadays, algorithms make feeds feel more alive and personal. Study shows that the technology behind Tiktok not only show content from accounts with a lot of followers, but they suggest videos based on how engaging they are, based on likes, shares and comments (Leminen et al, 2014). For users, this makes TikTok exciting and more fun.
But there is also a harmful side. Eventually, personal recommendations can turn into repetitive videos, since people are shown more of the same content which will be less diverse. When the feeds are not diverse, they tend to show more of the same, which creates an echo chamber (Zhou, 2024). Which can be manipulative, because you lose exposure to different views or content. This may shape your beliefs and preferences, and the algorithm may steer us into a direction which we never consciously would have chosen (Klenk, 2024). This limitation may lead to manipulation by the algorihtm.
In a nutshell, feeds makes it easier to discover content which makes social media enjoyable. But at the same time, it also limits what we see and push us toward choices we never would have made ourselves.
In my opinion, personalized feeds make social media more enjoyable, but at the same time it limits what we see and may push us to choices that are not really ours. The real challenge is to whether we can keep the fun of the platform without losing control of our senses.
References
Klenk, M. (2024). Ethics of generative AI and manipulation: a design-oriented research agenda. Ethics and Information Technology, 26(1). https://doi.org/10.1007/s10676-024-09745-x
Leminen, S., Westerlund, M., Sánchez, L., & Serra, A. (2014). Users as content creators, aggregators and distributors at Citilab Living Lab. In Edward Elgar Publishing eBooks. https://doi.org/10.4337/9781783475346.00020
Zhou, R. (2024). Understanding the impact of TikTok’s recommendation algorithm on user engagement. International Journal of Computer Science and Information Technology, 3(2), 201–208. https://doi.org/10.62051/ijcsit.v3n2.24
