Public Attitudes during the Second Lockdown: Sentiment and Topic Analyses using Tweets from Ontario, Canada (Preprint)
Bibliographic record
Abstract
BACKGROUND The COVID-19 pandemic has continued for over a year and caused a significant number of cases and deaths in Canada and around the world. Governments worldwide have implemented lockdowns and other interventions to reduce the transmission. OBJECTIVE To explore topics and sentiments using tweets from Ontario, Canada, during its second wave and identify any correlation between public attitude and government policy and cases. METHODS Tweets were collected from December 5, 2020, to March 6, 2021, with locations in Toronto and Ottawa, excluding accounts from organizations and individual political figures. Dates of vaccine-related events and COVID-19 policy changes were collected from each public health unit in Ontario within the same period. The daily number of COVID-19 cases was retrieved directly from the Ontario provincial government’s public health database within the same time period. Latent Dirichlet Allocation (LDA) was used for unsupervised topic modeling. Valence Aware Dictionary and sEntiment Reasoner (VADER) was used to calculate daily and average sentiment compound scores for topics identified. RESULTS Vaccine, pandemic, business, lockdown, mask, and Ontario were 6 topics identified from the unsupervised topic modeling. Between December 5, 2020, and March 6, 2021, the average sentiment compound score for each topic appeared to be slightly positive, while the daily sentiment compound scores varied greatly between positive and negative emotions for each topic. Positive sentiments were mainly about holiday wishes and support for healthcare works and each other, whereas negative sentiments were largely associated with frustrations and blame on political leaders. CONCLUSIONS Our study results have shown a slightly positive sentiment on average during the second wave of the COVID-19 pandemic in Ontario, along with six topics. Our research has also demonstrated a possible social listening approach to identify what the public sentiments and opinions are in a timely manner using a combination of quantitative and qualitative methods. CLINICALTRIAL
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".