Advancing Smart Cities Through Novel Social Media Text Analysis: A Case Study of Calgary
Bibliographic record
Abstract
In numerous cities, population expansion and technological advancements necessitate proactive modernization and integration of technology. However, the existing bureaucratic structure often hinders local officials' efforts to effectively address and monitor residents' needs and enhance the city accordingly. Understanding what people find important and useful can be inferred from their posts on social media. Twitter, as one of the most popular social media platforms, provides us with valuable data that, with the right tools and analysis, can provide insights into the performance of urban services and residents' perception of them. In this study, we used the city of Calgary as an exemplar to gather tweets and analyze topics relating to city development, urban planning, and minorities. Natural language processing (NLP) techniques were used and developed to preprocess stored tweets, classify the emotions, and identify the topics present in the dataset to eventually provide a set of topics with the prevalent emotion in that topic. We utilized a variety of methods to analyze the collected data. BERTopic for topic modeling and few-shot learning using Setfit for emotion analysis outperformed the others. Hence, we identify issues related to city development, senior citizens, taxes, and unemployment using these methods, and we demonstrate how delving into these analyses can improve urban planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".