Campaigning in the time of Twitter: 140 character ethical appeals from the 2012 United States presidential election
Bibliographic record
Abstract
This paper looked at the use of Twitter during the 2012 United States presidential campaign and the use of ethos appeals as a strategy to build credibility. As a new communication avenue, Twitter plays an unprecedented role in political discourse today. Both the Barack Obama and Mitt Romney campaigns have engaged in social media strategies and are actively using Twitter to communicate their talking points, and overall political platform. Larry Beason’s (1991) categories of signaled ethos were applied to examine a collection of tweets from each candidate. Sites like Twitter offer a more personal communication avenue for politicians to use. This paper discusses the strategic messaging on Twitter from politicians, and whether the messages contain ethos. The research questions explored are: to what extent are there ethos appeals on Twitter in the 2012 United States political candidates’ tweets? And, to what extent are particular ethos appeals prevalent? Of the 100 tweets examined from Barack Obama, the findings showed that 32% of his tweets contained ethos appeals, while 58% of the 100 tweets from Mitt Romney contained ethos appeals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".