Finfluencers on TikTok: A Longitudinal Analysis of Content, Engagement, and Disclaimer Practices

• URL: http://arxiv.org/abs/2607.05203v1
• Date: Mon, 06 Jul 2026 15:17:18 GMT
• Title: Finfluencers on TikTok: A Longitudinal Analysis of Content, Engagement, and Disclaimer Practices
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of social media financial influencers (finfluencers) has transformed how financial information is disseminated to broad and often inexperienced audiences. While these creators may contribute to financial literacy, concerns remain regarding the reliability of their content and the adequacy of risk disclosures. Using data collected through TikTok's Research API, we analyze UK finfluencer content, engagement dynamics, disclaimer practices, audience sentiment, and network structure. The primary dataset comprises 13,215 videos and 104,097 comments posted by 71 UK-based finfluencers between April and September 2024, while a follow-up dataset covering October 2025 to March 2026 enables longitudinal analysis of disclaimer practices, engagement trends, and hashtag usage. Using topic modeling, we identify four dominant themes: Entrepreneurship \& Side Hustles, Property Investing, Active Trading, and Saving \& Budgeting. Sentiment analysis of audience comments reveals predominantly neutral-to-positive responses, while engagement analysis shows only a negligible association between video duration and engagement rate. Social network analysis indicates a collaborative ecosystem in which mid-tier finfluencers frequently act as bridges between creator groups. Explicit disclaimers and risk-related language remain relatively uncommon overall and are concentrated primarily in trading-related content. The findings highlight challenges related to financial transparency and disclosure practices within short-form financial content ecosystems. We discuss implications for consumer protection and the design of clearer and more standardized financial risk disclosures on social media platforms.

Related papers

• Turning Trust to Transactions: Tracking Affiliate Marketing and FTC Compliance in YouTube's Influencer Economy [14.711670297954457]
  We analyze the prevalence of affiliate marketing on YouTube and the rates of non-compliant behavior.<n>Our findings reveal that affiliate links are widespread, yet dis- closure compliance remains low.<n>We recommend that regulators and affiliate partners collaborate with platforms to enhance transparency, accountability, and trust in the influencer economy.
  arXiv  Detail & Related papers  (2026-03-04T18:47:12Z)
• Anti-establishment sentiment on TikTok: Implications for understanding influence(rs) and expertise on social media [3.2580463372881234]
  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.
  arXiv  Detail & Related papers  (2025-08-22T15:13:35Z)
• 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)
• 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)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends [0.5249805590164902]
  We study whether semantic features in the form of contextual embeddings are more valuable than sentiment attributes for forecasting market trends. We consider the corpus of Twitter posts related to the largest companies by capitalization from NASDAQ and their close prices. Our results show that in the substantially prevailing number of cases, the use of sentiment features leads to higher metrics.
  arXiv  Detail & Related papers  (2023-03-24T18:30:15Z)
• Contextualizing Emerging Trends in Financial News Articles [2.9483477138814287]
  We focus on emerging trends detection in financial news articles about Microsoft, collected before and during the start of the COVID-19 pandemic. We make the dataset accessible and propose a strong baseline for exploring the dynamics of similarities between pairs of keywords. We evaluate against a gold standard (Google Trends) and present noteworthy real-world scenarios regarding the influence of the pandemic on Microsoft.
  arXiv  Detail & Related papers  (2023-01-20T12:56:52Z)
• 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)
• Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
  We argue that it is critical to understand what user attributes potentially cause users to share fake news. In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. We propose a principled approach to alleviating selection bias in fake news dissemination.
  arXiv  Detail & Related papers  (2020-10-20T19:37:04Z)
• 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)
• Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics [43.98568073610101]
  We use a social media model to quantify the impacts of several adversarial manipulation tactics on the quality of content. We find that the presence of influential accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
  arXiv  Detail & Related papers  (2019-07-13T21:12:08Z)

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.