The Research on the Impact of Fake News about Russia-Ukraine War upon Users
Bibliographic record
Abstract
In recent years, due to the constant changes in the international situation, fake news has experienced a increasing emerging, making it more difficult for users to distinguish between the truth. This article intends to conduct a research based on the recent fake news case of the Russia-Ukraine war, to focus on how the fake news impact on the mass public. The results of this research indicated that majority of the users would believe the message that the news conveyed, generated the negative attitudes of the audience, and further arouse users to criticize the “guilty party” that the news pointed out. However, throughout the research, this paper found out that there are certain number of users would question the realness of news, which this paper sees as a good phenomenon for it represents the awakening of civic consciousness. This paper calls for future experiments to start with how to improve the public's judgment, so as to reduce the bad influence of fake news on the public.
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How this classification was reachedexpand
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.001 | 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 teacher head, 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".