Understanding NFT Price Moves through Tweets Keywords Analysis
Bibliographic record
Abstract
The Non-Fungible Token (NFT) market is developing in tandem with the growth of the cryptocurrency market and the advancement of blockchain technologies. This has fostered a swiftly prospering NFT market, which subsequently entered a period of decline. Nevertheless, the overall rise procedure of the NFT market has not been well understood. We consider that evolving social media communities, accompanying market growth, offer valuable insights into market behaviours. Our research primarily focused on the NFT Twitter communities, conducting two experiments to gauge their impact on NFT price movements. Using a Granger causality test on tweet counts and NFT prices, we found that for most of the top 19 original projects, tweet volume positively influenced the price, or vice versa. This trend was seldom observed for copycat projects. To assess price movement predictability, we experimented with forecasting the Markov-normalized NFT price, indicative of price shift direction and magnitude, using tweet-extracted features. Social media words, as predictors, achieved testing accuracy above the baseline for all 19 top projects. Furthermore, both market-related and NFT event-related words notably contributed to price movement predictions. We summarized the characteristics of categorization and sentiment for the words with the most and least feature importance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".