Tweeting Power: The Communication of Leadership Roles on Prime Ministers’ Twitter
Bibliographic record
Abstract
This article examines the communication of leadership roles by prime ministers Justin Trudeau and Theresa May on Twitter. I argue that tweets from prime ministers implicitly communicate information about how prime ministers lead and what their job entails: what I call role performance and function. I develop an inductive typology of these leadership dimensions and apply this framework to Trudeau and May’s tweets in 2018 and 2019. I find first that Trudeau is a much more active Twitter user than Theresa May was as prime minister, attesting to different leadership styles. Second, both use Twitter primarily for publicity and to support and associate with individuals and groups. Trudeau is much more likely to use Twitter to portray himself as a non-political figure, while May is more likely to emphasize the role of policy ‘decider.’ Both prime ministers are framed much more often as national legislative leaders rather than party leaders or executives. Finally, May’s tweets reflect her position as an international leader much more than Trudeau’s. Assessing how prime ministers’ tweets reflect these dimensions contributes to our understanding of evolving leader–follower dynamics in the age of social media. While Twitter has been cited as conducive to populist leaders and rhetoric, this study shows how two non-populist leaders have adopted this medium, particularly in Trudeau’s case, to construct a personalized leader–follower relationship.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".