Which Came First, Price or Activity? A Vicious Circle of a Blockchain-Based Social Media in the Bear Market (Extended Abstract)
Bibliographic record
Abstract
This paper explores the relationship between the user activity on Steemit---the most widely used blockchain-based social media---and the price of STEEM---the cryptocurrency that can be earned from the activity on Steemit. Users can get STEEM by writing posts or upvoting ("liking") posts on Steemit. One may expect that activities on Steemit may affect the STEEM price, or conversely, the STEEM price may affect the user activity. We measure the Steemit activity by DAU (Daily Active Users) which is calculated as the number of unique users who write posts or comments on Steemit each day. We conduct the VAR (vector autoregressive) model analysis and the bidirectional Granger causality test on the STEEM price and the Steemit DAU for three different time regimes: full, bull-market, and bear-market regimes. We show that in all regimes, the STEEM price Granger causes the Steemit DAU. Conversely, the Steemit DAU does not Granger cause the STEEM price in both the full and bull-market regimes. In contrast, in the bear-market regime, the Steemit DAU Granger causes the STEEM price. That is, the STEEM price and the Steemit DAU Granger cause each other in the bear market, which can be seen as a vicious circle. We also show that the same results hold with the Hive blockchain, which is a fork of the Steem blockchain.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".