DELETING VIDEOS ON TIKTOK AS ALGORITHM RELATED IMPRESSION MANAGEMENT

Authors

  • Daniel Klug Carnegie Mellon University

DOI:

https://doi.org/10.5210/spir.v2022i0.13036

Keywords:

TikTok, algorithmic literacy,, creative practice, impression management

Abstract

On TikTok, users’ self-presentation is based on interaction and communication with and through short video content rather than on profile information or messaging. TikTok users commonly display awareness for how content is customized and develop strategies and video creation practices. Our qualitative interview study examines the practices of deleting videos and making videos private as forms of impression management online. We generally find that TikTok users deleted videos because the content or message did not represent their views or attitudes anymore or because they feared it would negatively affect their impression in offline reality. More experienced users also deleted videos in case they failed to achieve the desired feedback. We specifically identified impression management strategies related to participants’ algorithmic literacy. Participant’s assumptions of how the TikTok algorithm works influenced their decision to set videos to private instead of deleting them even if they only have small numbers of followers and likes or only occasionally post or interact with content. A main motivation to set a video to private was that deleting would mess up the users’ personalized algorithmic video feed and ultimately lead to missing trends they are looking for. Many participants also mentioned deleting videos would affect the impression the TikTok algorithm has of their profile and eventually push their videos to the wrong audiences. In this context, our results provide new insights into the effort and thought that users put into making short content on TikTok and how they manage this content to create online identities.

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Published

2023-03-30

How to Cite

Klug, D. (2023). DELETING VIDEOS ON TIKTOK AS ALGORITHM RELATED IMPRESSION MANAGEMENT. AoIR Selected Papers of Internet Research, 2022. https://doi.org/10.5210/spir.v2022i0.13036

Issue

Section

Papers K