Paper Info
Title
Trick and Please. A Mixed-Method Study On User Assumptions About the TikTok Algorithm
Abstract
ABSTRACTThe short-form video sharing app TikTok is characterized by content-based interactions that largely depend on individually customized video feeds curated by the app’s recommendation algorithm. Algorithms are generally invisible mechanisms within socio-technical systems that can influence how we perceive online and offline reality, and how we interact with each other. Based on experiences from consuming and creating videos, users develop assumptions about how the TikTok algorithm might work, and about how to trick and please the algorithm to make their videos trend so it pushes them to other users’ ‘for you’ pages. We conducted 28 qualitative interviews with TikTok users and identified three main criteria they assume influence the platform’s algorithm: video engagement, posting time, and adding and piling up hashtags. We then collected 300,617 videos from the TikTok trending section and performed a series of data exploration and analysis to test these user assumption by determining criteria for trending videos. Our data analysis confirms that higher video engagement through comments, likes, and shares leads to a higher chance of the algorithm pushing a video to the trending section. We also find that posting videos at certain times increases the chances of it trending and reaching higher popularity. In contrast, the highly common assumption that using trending hashtags, algorithm related hashtags (e.g. #fyp, #foryou), and piling up trending hashtags would significantly push videos to the trending section was found not applicable. Our results contribute to existing research on user understanding of social media algorithms using TikTok as an example for a short-video app that is explicitly built around algorithmic content recommendation. Our results provide a broader perspective on user beliefs and behavior in the context of socio-technical systems and social media content creation and consumption.
Year
DOI
Venue
2021
10.1145/3447535.3462512
Web Science
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Daniel Klug100.34
Yiluo Qin200.34
Morgan C. Evans300.68
Geoff F. Kaufman42113.00