An Ideological Analysis of Defamation in Selected YouTube Videos: A Critical Discourse Analysis Study
Bibliographic record
Abstract
The rapid growth of the Internet has an impact on many facets of daily life, including social communication. The prevalence of defamation on social media platforms is a significant component of this development. Through language, social groupings fight among themselves and promote their own views. Discourse indicates an ideology's influence based on this idea. This paper conducts a critical discourse analysis of defamation in selected YouTube videos. The study aims at examining the way a defamatory content published on YouTube affects the defamed person and detecting the hidden ideologies which motivate text producers to defame the target individual. In order to achieve the aims of this study, the researcher adopts a model for the sake of analysing the data from a critical discourse analysis perspective and analyses the data qualitatively to acquire a comprehensive critical understanding of the nature and traits of defamation in particular texts on YouTube. The collected texts are chosen from three defamatory videos published by Mr. Kevin J. Johnston. The researcher concludes that defamatory publications cover discrimination, prejudice, and racism; these ideologies are capable of motivating a person to defame and harm someone on social media platforms in general and on YouTube in particular.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".