The Drivers of Polarity in Sentiments on Social Media: an Exploratory Study on the 2021 Canadian Federal Election
Bibliographic record
Abstract
The drivers of polarity in sentiments on social media - an exploratory study on the 2021 Canadian Federal Election Hiba Mohammad Noor Master of Science in Management, 2022 Master of Science in Management, Ryerson University Social media is used by the public, voters, and politicians to share their political opinions leading to online political discourse. The opinions shared by voters on social media have different sentiments associated with them depending on voter needs and priorities. Understanding the factors that drive these sentiments can help policymakers and other political stakeholders to understand voter needs and expectations and develop policies that align with those needs. This research focuses on identifying the factors (keywords) that drive these sentiments. This research also investigates the relationship between these keywords and the number of retweets and hashtags. Sentiment analysis was performed on 779,169 tweets related to the 2021 Canadian Federal election followed by text clustering and keywords analysis. The topics and keywords that drive the sentiments were identified. Chi-Square test was used to investigate the relationship between these keywords, hashtags, and the number of retweets. The results suggest that some keywords are common in opposite sentiment types (positive and negative) which shows polarization in Twitter and some keywords are unique to a sentiment type which shows that these keywords drive that specific sentiment. The results also suggest that there is no significant relationship between the keywords and the number of hashtags but has a significant relationship between the keywords and the number of retweets for extremely negative tweets only.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".