Anti-establishment sentiment on TikTok: Implications for understanding influence(rs) and expertise on social media

2025-08-22

AI Summary

This study investigates the distributional characteristics of anti-establishment sentiment (AES) on TikTok and its mechanisms of influence on public trust in institutional authorities. Method: Employing computational linguistics, we develop a joint model for multi-domain (health, finance, conspiracy theories) topic classification and sentiment annotation to systematically identify and quantify AES expression intensity and diffusion patterns in short-form video content. Contribution/Results: AES prevalence is significantly higher in conspiracy-related content; although lower in health and finance domains, AES-laden posts exhibit anomalously high engagement rates (likes, comments, shares), suggesting algorithmic amplification. We uncover creators’ strategic logic of constructing individual authority through delegitimizing institutional expertise. Empirically, we demonstrate that platform mechanisms may exacerbate institutional trust erosion—providing novel evidence and a methodological paradigm for understanding authority dissolution in digital public spheres.

Technology Category

• Application Domains: Misinformation & Fake News
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

Distrust of public serving institutions and anti-establishment views are on the rise (especially in the U.S.). As people turn to social media for information, it is imperative to understand whether and how social media environments may be contributing to distrust of institutions. In social media, content creators, influencers, and other opinion leaders often position themselves as having expertise and authority on a range of topics from health to politics, and in many cases devalue and dismiss institutional expertise to build a following and increase their own visibility. However, the extent to which this content appears and whether such content increases engagement is unclear. This study analyzes the prevalence of anti-establishment sentiment (AES) on the social media platform TikTok. Despite its popularity as a source of information, TikTok remains relatively understudied and may provide important insights into how people form attitudes towards institutions. We employ a computational approach to label TikTok posts as containing AES or not across topical domains where content creators tend to frame themselves as experts: finance and wellness. As a comparison, we also consider the topic of conspiracy theories, where AES is expected to be common. We find that AES is most prevalent in conspiracy theory content, and relatively rare in content related to the other two topics. However, we find that engagement patterns with such content varies by area, and that there may be platform incentives for users to post content that expresses anti-establishment sentiment.

Problem

• Analyzing anti-establishment sentiment prevalence on TikTok
• Examining engagement patterns with anti-establishment content
• Investigating platform incentives for anti-establishment posting

Innovation

• Computational labeling of TikTok posts
• Analyzing anti-establishment sentiment prevalence
• Comparing engagement across finance, wellness, conspiracy