Equity crowdfunding platforms and social media: a Twitter analysis
Bibliographic record
Abstract
Purpose The purpose of this study is to explore the central users (hubs) in the dissemination of equity crowdfunding (EC) news on social media, with particular regard to Twitter. Specifically, the study explores some aspects related to the diffusion of news through social networks concerning EC. Design/methodology/approach Through a social network analysis (SNA) technique the authors define an understanding of the users' network that is created on Twitter when it comes to crowdfunding. Using Twitter data, the authors identify the central actors on the social network that produce and/or disseminate information about crowdfunding tools. Findings The results indicate that a large number of users tweeted about EC in relation to the introduction of the most important Commissione Nazionale per le Società e la Borsa (Consob) Regulation n. 20264 of 17/01/2018 on an equity model at the beginning of 2018; the growth in the use of this instrument in the first quarter of 2019 and the publication of Commissione Nazionale per le Società e la Borsa (Consob) Regulation n. 21110 of 10/10/2019. Moreover, the authors find that in the case of tweets concerning EC, the operators of the sector, with particular regard to crowdfunding platforms, are central to the network, followed by traditional and specialised media. Originality/value The results shed new light on a still unexplored research field concerning the diffusion of news about EC from a platform's perspective. To the best of the authors' knowledge, this is the first explorative study that jointly investigates an EC model and social media in the Italian market, considering the impacts of two different and important regulations. In particular, this study contributes to the literature on EC by clarifying some new aspects related to the diffusion of news through Twitter.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".