Social Construction of Blockchain on Social Media: Framing Public Discourses on Twitter
Bibliographic record
Abstract
Blockchain has become a hot topic in technology, finance, regulation, and the wider society in recent years. Along the way, various users and interests have shaped the technology materially and discursively. This paper investigates the debate taking place on Twitter surrounding blockchain technology to understand the nature and development of its online public discourses. We collected and analyzed a Twitter dataset containing a total of 267,512 tweets that reference blockchain by 105,734 unique users. We conducted a mixed method research study involving qualitative and quantitative approaches. The results indicate that the majority of the retweeted posts are educational and promotional in nature, while the lowest numbers of frames are critical or skeptical of the new technology. The most active users seem to be largely involved in promoting the technology including some that are human created bots. The paper employs the theory of Social Construction of Technology (SCOT) that emphasizes the way our actions and discourses shape technology. We argue that a number of active Twitter users, for a variety of motives including financial ones, are shaping the discourse about blockchain by mostly framing it as a positive development in the global market, allegedly creating a revolution in the financial sector. More importantly, the social construction of technology on Twitter does not seem to be exclusively organic, for it includes bots and online spammers who mostly tweet promotional blockchain hashtags.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 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".