Hidden Social Networks that Drive Online Public Involvement in Infrastructure Construction
Bibliographic record
Abstract
Several studies have emphasized the role of social media in public involvement during planning, construction, and operation of infrastructure. Social media can offer optimal, bidirectional communication with the public and an efficient flow of information and feedback between decision makers and project end users. However, the underlying social networks formed in the background of public relations processes on online social media largely have been neglected. Such networks are referred to as infrastructure discussion networks (IDN) and can be rich sources of information for decision makers. If it can be proved that these networks follow the general behavior of social networks, then rich tools developed in social network analysis can be used to profile users and help decision makers study user attitudes. This paper compares the IDNs of four light rail transit projects in various cities in the United States and Canada and studies them under the microscope of social network analysis. The study shows that IDNs exhibit the general structure of social networks (such as small-world behavior, scale-free degree distribution, and high clustering). The IDNs also include meaningful communities, detection and evaluation of which could lead to more efficient offline and online public involvement processes. Given the recent trends in presence of infrastructure projects on online social media, the methods presented in this paper can be readily applied by practitioners to improve their public engagement practices.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".