Tweeting Power: The Communication of Leadership Roles on Prime Ministers’ Twitter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it