Peer-to-Peer Decentralized Social Media Platform Using Second-Layer Blockchain Technology
Bibliographic record
Abstract
Current social media platforms built on specialized blockchains exist, but have failed to reach mainstream adoption. These decentralized alternatives have not been able to successfully migrate users from the existing offering of centralized platforms due in part to their decisions to create their own specialized currency and blockchain. This study proposes the idea of an algorithmically decentralized social media platform running via a second-layer extension of the blockchain as a solution that would alleviate the failures of previous blockchain iterations, and return social media to a peer-to-peer platform open to everyone as a way to dissipate knowledge, and provide services regardless of political constraints, borders, or personal beliefs. By analyzing this new technology, this thesis explores the opportunities and advantages that second-layer blockchain protocol solutions provide to address the vital concerns, and criticisms of the current internet while addressing which group of users are more likely to understand and favor them. Tweets were extracted and analyzed, finding that individuals are increasingly becoming interested in Bitcoin and second-layer technologies rather than dedicated and segregated projects that have been decreasing in online popularity. The study’s geographic, historical, and economic analysis suggests that in order for decentralized social media platforms to stay truly decentralized and to reach mainstream adoption, they will need to integrate with the Bitcoin blockchain to build on the already established momentum. The second-layer solution being proposed by this study solves technological complications and increases the likelihood of mainstream adoption.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".