Polarization and Disinformation in the Context of the Letter Revolution in Türkiye: Analyzing the Dynamics of X
Bibliographic record
Abstract
This study examines the dynamics of disinformation spread on X (formerly Twitter) in the context of Türkiye’s Letter Revolution, employing the Hypodermic Needle Model and Cultivation Theory as primary theoretical frameworks. By applying these theories to social media disinformation, we offer a novel approach to understanding both the immediate impact and long-term effects of false narratives on public perception. Our research analyzes a unique dataset of 36.9 million tweets from accounts closed due to disinformation spread, focusing on the #harfdevrimi hashtag. The purpose of this paper is to demonstrate how these communication theories can be effectively applied to modern digital environments, providing insights into the mechanisms of disinformation dissemination and its role in societal polarization. A comprehensive theoretical framework for analyzing social media disinformation, methodological innovations in large-scale social media data analysis, and critical insights into the challenges of maintaining an informed citizenry in politically sensitive contexts will be presented. This research contributes to the growing body of literature on media manipulation and offers a model for future studies on disinformation in digital spaces.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".