WHAT DOES SOCIAL MEDIA SAY ABOUT THE INFRASTRUCTURE CONSTRUCTION PROJECT
Bibliographic record
Abstract
The process of public consultation for planning and construction of the urban infrastructure as a sociotechnical system requires a bidirectional interaction and dialogue among all stakeholders of the project. Policy makers, official and technical decision makers, and the public should get together in form of a social network to discuss different aspects of the project. Social media and the social Web can play the role of a platform to accommodate a social network and keep the flow of related discussions. Detecting cores of interest formed in such networks and profiling stakeholders of the project (including the end users) can help decision makers in the process of demand detection, public engagement, and marketing the project to the public community. This requires topology analysis of the social connections and semantic analysis of the discussions. This paper introduces some initial steps in this regard. It combines community detection algorithms with information retrieval practices into a hybrid technique to detect and profile the communities of the project followers and cores of interest in the network of stakeholders of the urban infrastructure project. This technique is used to analyze micro-blogging website Twitter for crosstown LRT project to profile the followers based on their interests and the ideas they support. Analysis of the content and sentiment of the discussions is currently an underway research and would be discussed elsewhere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".