An Analysis of the Media Messages during the 2016 U.S. Presidential Election: A Thematic Comparison between CNN News and Donald Trump’s Tweets
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
In the last presidential election of the United States (2016), the interaction between the Donald Trump and the American media was remarkable and unprecedented from both political and communication perspectives. The present paper is interested in observing the interactions between then the Republican Party candidate, Donald Trump, and the media of the United States. As there were major verbal confrontations between Trump, and some media, specifically CNN, this paper observes how Trump campaign reacted to CNN that turned out to be one of his biggest opponents. The relations and reactions are explained using “agenda setting” and “selective exposure” as theories and “thematic analysis” as the research methodology. The paper analyzes CNN videos from October 7 to October 31, and Trump’s tweets during the same period. The reason for conducting the research during October is that this month is regarded as one of the most critical periods in US presidential election, known as “October surprise”. Then a thematic analysis of the data is conducted to extract all accusations and allegations against Trump. Research results show that President Trump did not react to most of the accusations and attacks raised by CNN. Apparently Trump had decided that ignoring and not responding is a better strategy. There was an exception to this rule: Trump’s treatment toward women. He did address that issue frequently and tried to justify himself and apologize. Accordingly, Trump’s presidential campaign aim was to ignore accusations, keep attacking, and answering accusations only if they are already known to too many people.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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