Using TikTok as a Search Engine: Affordances, Perceived Credibility, and Evaluative Actions

Abstract

Based on 494 survey responses (N = 494), we draw on the uses and gratifications (U&G) framework and affordance theory to examine TikTok users’ information-searching behaviors on the platform, as well as technical affordances that motivate these behaviors. Results demonstrate that, as a search engine, TikTok is perceived as less valuable than traditional search engines (e.g., Google) in terms of convenience, reassurance, independence, privacy, and functionality. Meanwhile, evaluative actions primarily focus on checking others’ comments and video elements rather than checking other official outside sources. Live-streaming and algorithm-driven recommendations significantly impact users’ perceived credibility of information found on TikTok and their sense of social presence. Algorithm-based recommendations also play a crucial role in shaping users’ trust in content and their verification behaviors. Contrary to expectations, meta-voicing (i.e., users’ engagement via the comment section and reactions) does not predict social presence, which hints at the need to refine existing affordance frameworks for short-form video platforms. Social presence and perceived source credibility, in turn, significantly predict information-seeking behaviors. Interestingly, higher perceived credibility leads to more evaluative actions being taken to reassess information found on TikTok. Empirically, the findings have implications for information literacy initiatives, platform design, and algorithmic transparency to understand users’ searching behaviors and how they evaluate information credibility on social media platforms. Theoretical implications of the findings, as well as limitations and suggestions for future research, are further discussed.