TikTok Search Recommendations: Governance and Research Challenges

2025-05-13

AI Summary

TikTok’s transformation of short-video platforms into search engines has introduced novel governance challenges, as its pre-generated search recommendations lack transparency, accountability, and contextual sensitivity. This paper presents the first systematic analysis of governance risks arising from social media search recommendation systems. We develop a problem-driven research framework integrating qualitative analysis, regulatory frameworks—including the EU’s Digital Services Act—and platform governance theory. Our key contribution is a tripartite computational research agenda centered on *explainability*, *accountability*, and *interdisciplinary collaboration*. We propose three concrete policy pathways: (1) mandatory platform documentation transparency, (2) enhanced accessibility of algorithmic inputs and outputs for researchers and regulators, and (3) institutionalized support for independent third-party algorithmic audits. The findings provide both theoretical grounding and actionable guidance for regulatory technology development and algorithmic governance in digital platforms. (149 words)

Technology Category

• Search and Optimization: Algorithm Configuration
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Application Domains: Humanities & Computational Social Science

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

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.

Problem

• TikTok lacks transparency in search recommendation algorithms
• Contextually problematic recommendations raise platform liability concerns
• Research needed for governance and moderation of search features

Innovation

• Preformulated search queries recommended on videos
• Limited transparency in search recommendation generation
• Preliminary qualitative analysis for research agenda