Masks and Caricatures: Prosopopoeia, Ethopoeia, and the Effect of Social Media on Canadian Political Leaders’ Debates
Bibliographic record
Abstract
This dissertation examines the recent effect of social media on televised political leaders’ debates through the lens of ethos. It features two case studies from two Canadian federal elections: the 2015 Maclean’s Leaders’ Debate, and the 2019 English-Language Leaders’ Debate. It opens the lens of ethos through the tools of prosopopoeia and ethopoeia, ethotic moves which respectively incorporate ethoi beyond the immediate speaker, and characterize the ethoi of others. With the emergence of participatory digital media, leadership debates are increasingly constrained and shaped to serve social media. I argue that there is an increased pressure on political parties to have their leader adopt a mask, or perform another’s ethos, through prosopopoeia, while also characterizing, or depicting another’s ethos, through ethopoeia. Both moves capitalize on the Aristotelian ethotic qualities of phronesis, arete, and eunoia. I develop this argument by analyzing political parties’ and political leaders’ debate-related social media posts from Canada’s 2015 and Canada’s 2019 federal elections. I examine political parties’ and political leaders’ debate-related posts on three social media platforms: Facebook, Twitter, and Instagram, from the 2015 and the 2019 election campaign timeframes. In examining the parties’ and leaders’ top shared Facebook posts, top retweeted Twitter posts, and top liked Instagram posts, I identify six major debate-related themes for 2015, and six major debate-related themes for 2019. Examining the posts within these themes reveals how ethos is refracted in social media, moderately in 2015, and vigorously in 2019, and how the rhetorical moves prosopopoeia and ethopoeia infiltrate the political parties’ and leaders’ social media accounts. A significant finding of this study is political debates are changing because of social media in a way that foregrounds issues of ethos. In 2015, it was more prominent for debate content to move out onto social media, whereas in 2019, debate content is being shaped for social media. In both cases, but more so in 2019, the forces of social media fostered prosopopoeia and ethopoeia. This research contributes to the fields of rhetoric, social media, and political communication by demonstrating how debates, and democracy, are being (re)shaped by social media, and brings precision to the rhetorical figures prosopopoeia and ethopoeia as figures of argumentation. This critical investigation into the effect of social media on political leaders’ debates reveals the rhetorical influence social media has on political parties, political leaders, and ultimately voters.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".