AI & FINTECH
Unlocking Cryptocurrency FoMO: A Dual-Source, AI-Driven Approach to Market Sentiment
This analysis delves into CVT-FOMO, a novel framework designed to address the scarcity of fine-grained, dual-source data in cryptocurrency Fear of Missing Out (FoMO) research. By integrating advanced prompt engineering with Large Language Models (LLMs), CVT-FOMO effectively analyzes YouTube video subtitles and comments to identify and categorize FoMO signals, providing crucial insights into market dynamics and investor psychology. Our findings offer a robust benchmark for sentiment analysis and platform risk governance, validating an 'Induction-Resonance' mechanism where creator agendas are statistically amplified by audience interactions.
Key Metrics at a Glance
The cryptocurrency market, characterized by high volatility and emotional decision-making, acutely amplifies psychological drivers like Fear of Missing Out (FoMO). Traditional methods struggle with the multidimensionality of FoMO, isolated data sources, and LLM semantic ambiguity in financial contexts. CVT-FOMO introduces a dual-source dataset and a triple-combination prompting strategy to overcome these challenges, achieving high accuracy and revealing asset-specific FoMO drivers.
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Human Win Rate
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Dataset Pairs
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Induction-Resonance Coefficient
Deep Analysis & Enterprise Applications
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Methodology
Key Findings
Applications
The CVT-FOMO framework addresses high-noise FoMO signals through an end-to-end pipeline leveraging LLMs. It maps unstructured video transcripts (creator narratives) and user comments (audience psychology) to fine-grained labels. The process involves Data Ingestion & Decoupling, Cross-Source Semantic Consensus Verification using an LLM-based prompting strategy, and Fine-Grained Labeling. This establishes a Bidirectional Textual Corroboration criterion, filtering noise and ensuring high signal-to-noise ratio. A critical component is the Triple-Combination Prompting Strategy (RP+FS+CoT), integrating Role-Play, Few-Shot, and Chain-of-Thought paradigms for precise extraction of fine-grained FoMO features and mitigation of LLM hallucinations.
Experiments demonstrated that the Triple-Combination Strategy (RP+FS+CoT) achieved an 85% human win rate on GPT-40, significantly outperforming dual (68.3%) and single strategies (45.7%). This robust performance stems from its Cognitive Scaffolding: Context Anchoring (RP) injects domain knowledge, Paradigm Alignment (FS) provides soft constraints with exemplars, and Logical Explicitization (CoT) enforces cross-modal logical self-consistency. Semantic coupling analysis revealed an 81.02% average diagonal matching rate between video stimuli and comment responses, confirming effective agenda-setting. Notably, a 'Value Regression' Semantic Drift was observed, where ~12.7-12.9% of responses deviated to the 'Investment Opportunity' category, indicating an underlying psychology focused on price. Furthermore, the Resonant Amplification Effect was confirmed with an emotion amplification coefficient of 0.28, demonstrating that social media comments act as 'gain amplifiers' for market anxiety, turning single creator stimuli into 'collective frenzy'.
The CVT-FOMO framework and dataset provide actionable evidence for enhanced investor education, platform risk governance, and advanced sentiment analysis. By understanding the asset-specific FoMO drivers (e.g., Bitcoin: investment opportunity anxiety; Ethereum: technological fear; Dogecoin: meme culture), financial platforms can tailor warnings and educational content. The validated 'Induction-Resonance' mechanism highlights the need for proactive moderation of influencer content to mitigate the amplification of market anxiety. The framework’s cross-lingual robustness (demonstrated across Chinese and English) suggests its applicability in diverse global markets without extensive language-specific fine-tuning, paving the way for international risk management strategies and more accurate predictive models for cryptocurrency volatility.
Enterprise Process Flow
Data Ingestion & Decoupling
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Cross-Source Semantic Consensus Verification
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Fine-Grained Labeling
85
Human Win Rate for RP+FS+CoT Strategy
Asset-Specific FoMO Drivers
- Bitcoin (Value Consensus): Heavily dominated by 'Investment Opportunity' (40.03%), linking price breakthroughs or Halving cycles to anxiety about 'class transcendence'.
- Ethereum (Technical Ecosystem): High frequency of 'Market Trends & Tech' (32.84%), driven by fear of missing Tech Dividends due to 'technological lag'. Lower reliance on 'Social Signals' (8.88%) implies focus on fundamental analysis.
- Dogecoin (Meme Narrative): Exhibits extreme 'High Speculation, Low Technology' traits, with 'Investment Opportunity' peaking at 47.33% and 'Emerging Projects' at 22.50%, driven by 'get-rich-quick' myths and constant chase for novel narratives. 'Market Trends' share is only 17.57%, confirming disregard for technical fundamentals.
0.28
Emotion Amplification Coefficient (k)
Comparison of Prompting Strategies
| Strategy | Key Advantages |
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| Single Strategies (RP, FS, CoT) |
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| Dual Strategies (RP+CoT, RP+FS, etc.) |
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| Triple-Combination (RP+FS+CoT) |
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