Anti-establishment sentiment on TikTok: Implications for understanding influence(rs) and expertise on social media
- URL: http://arxiv.org/abs/2508.16453v1
- Date: Fri, 22 Aug 2025 15:13:35 GMT
- Title: Anti-establishment sentiment on TikTok: Implications for understanding influence(rs) and expertise on social media
- Abstract summary: This study analyzes the prevalence of anti-establishment sentiment (AES) on the social media platform TikTok.<n>We find that AES is most prevalent in conspiracy theory content, and relatively rare in content related to the other two topics.
- Score: 3.2580463372881234
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Finfluencers on TikTok: A Longitudinal Analysis of Content, Engagement, and Disclaimer Practices [0.0]
We analyze UK finfluencer content, engagement dynamics, disclaimer practices, audience sentiment, and network structure.<n>Using topic modeling, we identify four dominant themes: Entrepreneurship & Side Hustles, Property Investing, Active Trading, and Saving & Budgeting.<n>We discuss implications for consumer protection and the design of clearer and more standardized financial risk disclosures on social media platforms.
arXiv Detail & Related papers (2026-07-06T15:17:18Z) - ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety [87.90209836101353]
ConspirED is the first dataset of conspiratorial content annotated for general cognitive traits.<n>We develop computational models that identify conspiratorial traits and determine dominant traits in text excerpts.<n>We evaluate large language/reasoning model (LLM/LRM) robustness to conspiratorial inputs.
arXiv Detail & Related papers (2025-08-28T06:39:25Z) - Community Moderation and the New Epistemology of Fact Checking on Social Media [124.26693978503339]
Social media platforms have traditionally relied on independent fact-checking organizations to identify and flag misleading content.<n>X (formerly Twitter) and Meta have shifted towards community-driven content moderation by launching their own versions of crowd-sourced fact-checking.<n>We examine the current approaches to misinformation detection across major platforms, explore the emerging role of community-driven moderation, and critically evaluate both the promises and challenges of crowd-checking at scale.
arXiv Detail & Related papers (2025-05-26T14:50:18Z) - UK Finfluencers: Exploring Content, Reach, and Responsibility [0.0]
This study investigates the patterns and behaviours of finfluencers in the UK on TikTok.<n>The aim is to identify common engagement patterns and propose guidelines that can help protect the public from potential financial harm.
arXiv Detail & Related papers (2025-05-03T22:28:36Z) - Analyzing News Engagement on Facebook: Tracking Ideological Segregation and News Quality in the Facebook URL Dataset [3.443622476405787]
The dataset has been used in several studies to analyze the relationship between social media engagement and societal issues such as misinformation, polarization, and the quality of consumed news.<n>We conduct a comprehensive analysis of the engagement with popular news domains, covering four years from January 2017 to December 2020, with a focus on user engagement metrics related to news URLs in the U.S.<n>Our findings provide empirical evidence to better understand user behavior, polarization, and misinformation during the period covered by the dataset.
arXiv Detail & Related papers (2024-09-20T12:50:17Z) - An integrated view of Quantum Technology? Mapping Media, Business, and Policy Narratives [0.0]
This study examines how QT is presented in business, media, and government texts using thematic narrative analysis.
We employ a computational social science approach, combining BERTopic modeling with qualitative assessment to extract themes and narratives.
The findings show that public discourse on QT reflects prevailing social and political agendas, focusing on technical and commercial potential, global conflicts, national strategies, and social issues.
arXiv Detail & Related papers (2024-08-05T05:00:57Z) - Authenticity and exclusion: social media algorithms and the dynamics of belonging in epistemic communities [0.8287206589886879]
This paper examines how social media platforms and their recommendation algorithms shape the professional visibility and opportunities of researchers from minority groups.
Using agent-based simulations, we uncover three key patterns: First, these algorithms disproportionately harm the professional visibility of researchers from minority groups.
Second, within these minority groups, the algorithms result in greater visibility for users who more closely resemble the majority group, incentivizing assimilation at the cost of professional invisibility.
arXiv Detail & Related papers (2024-07-11T14:36:58Z) - GPT-4V(ision) as A Social Media Analysis Engine [77.23394183063238]
This paper explores GPT-4V's capabilities for social multimedia analysis.
We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection.
GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge.
arXiv Detail & Related papers (2023-11-13T18:36:50Z) - Understanding Divergent Framing of the Supreme Court Controversies:
Social Media vs. News Outlets [56.67097829383139]
We focus on the nuanced distinctions in framing of social media and traditional media outlets concerning a series of U.S. Supreme Court rulings.
We observe significant polarization in the news media's treatment of affirmative action and abortion rights, whereas the topic of student loans tends to exhibit a greater degree of consensus.
arXiv Detail & Related papers (2023-09-18T06:40:21Z) - What Do We Mean When We Talk about Trust in Social Media? A Systematic
Review [18.634186284628694]
Trust in social media is a complex concept, and there is conflicting evidence about the antecedents and implications of trusting social media content, users, and platforms.
We conducted a systematic review to identify themes and challenges in this field.
We contribute a synthesis of how trust in social media is defined, conceptualized, and measured, a summary of trust antecedents in social media, an understanding of how trust in social media impacts behaviors and attitudes, and directions for future work.
arXiv Detail & Related papers (2023-02-07T18:45:43Z) - The Impact of Disinformation on a Controversial Debate on Social Media [1.299941371793082]
We study how pervasive is the presence of disinformation in the Italian debate around immigration on Twitter.
By characterising the Twitter users with an textitUntrustworthiness score, we are able to see that such bad information consumption habits are not equally distributed across the users.
arXiv Detail & Related papers (2021-06-30T10:29:07Z) - News consumption and social media regulations policy [70.31753171707005]
We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation.
Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content.
The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
arXiv Detail & Related papers (2021-06-07T19:26:32Z) - Information Consumption and Social Response in a Segregated Environment:
the Case of Gab [74.5095691235917]
This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic.
We find that there are no strong statistical differences in the social response to questionable and reliable content.
Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
arXiv Detail & Related papers (2020-06-03T11:34:25Z) - Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms.
We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features.
We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.