The Internet Research Agency Campaign to Influence the 2016 <scp>US</scp> Presidential Elections: A Rhetorical Analysis
Bibliographic record
Abstract
ABSTRACT The centrality of information and communicative processes in persuading society has, historically, made the media one of the key networks of power and influence in society. The rapid expansion of social media platforms has, however, enabled revolutionary changes in how this power is wielded and how persuasion occurs. This has had a profound impact on how political, economic, and social issues are understood and addressed. While a comprehensive body of social psychological theory and applied practice on the topic of persuasion has been developed over many years, persuasion in the contemporary social media environment is one that researchers are yet to fully understand. Methods for achieving this understanding continue to evolve. This article draws on a large corpus of material (2218 Facebook advertisements and metadata) which documented the Russian Internet Research Agency campaign to influence the outcome of the 2016 US presidential elections. Drawing on Aristotle's rhetorical framework, this article presents a process analysis to understand how political persuasion is undertaken in the contemporary social media environment. The findings provide new insights into the social psychological processes of persuasion in contemporary society and demonstrate the utility of a rhetorical framework in understanding persuasion campaigns in dynamic digital settings.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 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".