TikTok Search Recommendations: Governance and Research Challenges
For platform governance researchers and regulators, this paper highlights a novel feature that exacerbates existing concerns about platform liability and moderation, but it is primarily a call to action rather than providing empirical results.
This position paper identifies governance and research challenges posed by TikTok's search recommendation feature, which suggests preformulated queries to users. The authors argue that TikTok's lack of transparency and reliance on aggregated comments/common searches sidesteps responsibility for problematic recommendations, and they propose a computational research agenda to address these issues.
Like other social media, TikTok is embracing its use as a search engine, developing search products to steer users to produce searchable content and engage in content discovery. Their recently developed product search recommendations are preformulated search queries recommended to users on videos. However, TikTok provides limited transparency about how search recommendations are generated and moderated, despite requirements under regulatory frameworks like the European Union's Digital Services Act. By suggesting that the platform simply aggregates comments and common searches linked to videos, it sidesteps responsibility and issues that arise from contextually problematic recommendations, reigniting long-standing concerns about platform liability and moderation. This position paper addresses the novelty of search recommendations on TikTok by highlighting the challenges that this feature poses for platform governance and offering a computational research agenda, drawing on preliminary qualitative analysis. It sets out the need for transparency in platform documentation, data access and research to study search recommendations.