Informing Government Decision-Making with Online Citizen Feedback and Social Media: Pedestrianization of Streets
Bibliographic record
Abstract
The rise of social media and online platforms allowed citizens to share thoughts and feedback in a digital format, which opens the potential for governments and stakeholders to use data mining to inform design, implementation, and monitoring of public policies and projects. This study presents a case of data mining and analysis of citizen feedback data from multiple platforms: Online survey; Social Media; and Citizen Assembly; to inform policy and decision-making of the Intramuros Administration regarding a proposed pedestrianization of a major street. The study applies a CRISP-DM data mining methodology to pre-process and process feedback data from Facebook, Pol.Is survey platform, and two citizen assemblies, to highlight the key concerns and priorities of constituents regarding the policy topic. Using timeline analysis, principal components analysis, clustering, association rules mining, and topic modeling, the priority concerns of the stakeholders regarding the policy were found to be: security and safety while walking, negative effects of pedestrianization on business, concerns about parking spaces, alternative routes, and accessibility (PWDs and senior citizens). Using the findings as a centerpiece for stakeholder dialogue, the Intramuros Administration and stakeholders discussed in detail, and co-created the proposed next steps to address the concerns raised. The study presents the Intramuros survey case as a replicable model for automation and integration of citizen feedback data in local government policy and decision-making.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".