Credibility Matters: Motivations, Characteristics, and Influence Mechanisms of Crypto Key Opinion Leaders

• URL: http://arxiv.org/abs/2603.12000v1
• Date: Thu, 12 Mar 2026 14:47:47 GMT
• Title: Credibility Matters: Motivations, Characteristics, and Influence Mechanisms of Crypto Key Opinion Leaders
• Abstract summary: Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour.<n>In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers.<n>We examine how psychological needs are negotiated alongside monetisation and community expectations.
• Score: 42.24343564886025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Crypto Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour. In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers. Yet prior research has focused on lifestyle influencers or generic financial commentary, leaving crypto KOLs' understandings of motivation, credibility, and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psychological needs are negotiated alongside monetisation and community expectations. Whereas prior work treats finfluencer credibility as a set of static credentials, our findings reveal it to be a self-determined, ethically enacted practice. We identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction. This reframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically, we employ a hybrid human-LLM thematic analysis. The study surfaces implications for designing credibility signals that prioritise transparency over hype.

Related papers

• Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs [60.80435138270317]
  We apply reinforcement learning with metacognitive feedback to the problem of faithful calibration.<n>Experiments show RLMF achieves generalizable, state-of-the-art FC on diverse tasks.<n> RLMF surpasses standard RL by up to 63% while enhancing models' ability to assess and express their own capability limits.
  arXiv  Detail & Related papers  (2026-06-30T17:56:01Z)
• Prediction Laundering: The Illusion of Neutrality, Transparency, and Governance in Polymarket [17.356126657763806]
  This paper presents a qualitative sociotechnical audit of Polymarket (N = 27), combining digital ethnography, interpretive walkthroughs, and semi-structured interviews.<n>We show how subjective, high-uncertainty bets, strategic hedges, and capital-heavy whale activity are stripped of their original noise through algorithmic aggregation.
  arXiv  Detail & Related papers  (2026-02-05T01:19:25Z)
• A Decentralized Framework for Ethical Authorship Validation in Academic Publishing: Leveraging Self-Sovereign Identity and Blockchain Technology [0.0]
  Unconsented authorship, gift authorship, author ambiguity, and undisclosed conflicts of interest threaten academic publishing.<n>This paper introduces a decentralized framework leveraging Self-Sovereign Identity (SSI) and blockchain technology.<n>A blockchain-based trust registry records authorship consent and peer-review activity immutably.<n>This work represents a step toward a more transparent, accountable, and trustworthy academic publishing ecosystem.
  arXiv  Detail & Related papers  (2025-08-03T20:26:19Z)
• Truly Self-Improving Agents Require Intrinsic Metacognitive Learning [59.60803539959191]
  Self-improving agents aim to continuously acquire new capabilities with minimal supervision.<n>Current approaches face two key limitations: their self-improvement processes are often rigid, fail to generalize across tasks domains, and struggle to scale with increasing agent capabilities.<n>We argue that effective self-improvement requires intrinsic metacognitive learning, defined as an agent's intrinsic ability to actively evaluate, reflect on, and adapt its own learning processes.
  arXiv  Detail & Related papers  (2025-06-05T14:53:35Z)
• Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models [91.24296813969003]
  This paper advocates integrating causal methods into machine learning to navigate the trade-offs among key principles of trustworthy ML.<n>We argue that a causal approach is essential for balancing multiple competing objectives in both trustworthy ML and foundation models.
  arXiv  Detail & Related papers  (2025-02-28T14:57:33Z)
• Synthetic Social Media Influence Experimentation via an Agentic Reinforcement Learning Large Language Model Bot [7.242974711907219]
  This study provides a novel simulated environment that combines agentic intelligence with Large Language Models (LLMs) to test topic-specific influence mechanisms.<n>Our framework contains agents that generate posts, form opinions on specific topics, and socially follow/unfollow each other based on the outcome of discussions.
  arXiv  Detail & Related papers  (2024-11-29T11:37:12Z)
• A Game-Theoretic Approach to the Study of Blockchain's Robustness [0.0]
  This thesis investigates the robustness of blockchain protocols, specifically focusing on Proof-of-Stake.<n>We define robustness in terms of two critical properties: Safety, which ensures that the blockchain will not have permanent conflicting blocks, and Liveness, which guarantees the continuous addition of new reliable blocks.<n>Our research addresses the gap between traditional distributed systems approaches, which classify agents as either honest or Byzantine (i.e. malicious or faulty), and game-theoretic models that consider rational agents driven by incentives.
  arXiv  Detail & Related papers  (2024-11-28T14:29:14Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Belief in the Machine: Investigating Epistemological Blind Spots of Language Models [51.63547465454027]
  Language models (LMs) are essential for reliable decision-making in fields like healthcare, law, and journalism. This study systematically evaluates the capabilities of modern LMs, including GPT-4, Claude-3, and Llama-3, using a new dataset, KaBLE. Our results reveal key limitations. First, while LMs achieve 86% accuracy on factual scenarios, their performance drops significantly with false scenarios. Second, LMs struggle with recognizing and affirming personal beliefs, especially when those beliefs contradict factual data.
  arXiv  Detail & Related papers  (2024-10-28T16:38:20Z)
• Trust Dynamics and Market Behavior in Cryptocurrency: A Comparative Study of Centralized and Decentralized Exchanges [1.9624273277521183]
  The collapse of FTX, a major CEX, provides a unique natural experiment to examine the measurable impacts of trust and its sudden erosion on the cryptocurrency ecosystem.<n>This research investigates the impacts of the FTX collapse on user trust, focusing on token valuation, trading flows, and sentiment dynamics.
  arXiv  Detail & Related papers  (2024-04-26T07:58:05Z)

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.