Topic modeling for infrastructure-related discussions in online social media
Bibliographic record
Abstract
Decision making for construction of modern civil infrastructure not only involves internal stakeholders, but also aims to include interests of as many external stakeholders as possible. In mega-projects, complexity and diversity of stakeholders call for more advanced communication tools and channels. Extensive prevalence of social web as a two-way communication channel during the last decade has caused a paradigm shift in communication among the e-society, and this has attracted the attention of decision makers in the domain of urban infrastructure among other domains. Although having a wide public outreach, the open and unstructured nature of inputs from the e-society results in chaos and makes it difficult to distil knowledge from the contents communicated by the public. This paper presents tools from topic modeling to process such an unstructured data collected from online social media into information which can be plugged into the process of decision making. We use k-means clustering to cluster followers of an infrastructure project on micro-blogging website Twitter based on semantic similarity among their user profile descriptions. This helps profiling the main groups of followers of the infrastructure project and can provide decision makers with valuable hints regarding typology of external stakeholders. We also extend our analysis to project-related tweets through Latent Semantic Indexing, and find the main topics discussed. The latter guide help decision makers understand the public’s major vested interests in the project. We have applied the proposed method to a Light Rail Transit (LRT) mega-project in Toronto, Ontario and have discussed the results.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".