A systematic review for netizens’ response to the truth manipulation on social media
Bibliographic record
Abstract
The manipulated or manufactured truth on social media platforms spreads false information to influence netizens’ cognition, often resulting in fabricated social and political narratives. This study systematically reviews the literature on truth manipulation and its impact on the cognition of social media users. The primary focus is on disinformation, misinformation, fake news, and propaganda. The study appraises 162 peer-reviewed publications indexed in the Web of Science Core Collection database using the systematic review method. The data was put through a bibliometric analysis to unpack the evolutionary nuances of netizens’ cognitive response to manufactured truth, informativity, and manipulation on social media. The study highlights emerging trends and issues from truth manipulation on social media. The bibliometric analysis reveals since 2017, there has been an increase in the trend of scholarly work about truth manipulation on social media and its effects on the cognition of netizens. The USA seems to be the most prominent node to contribute to the study of truth manipulation. The content analysis shows multiple aspects causing truth manipulation. This study also seeks ways and methods to prevent and counter truth manipulation on social media. It looks at the possibilities of altering netizens’ cognitive abilities by improving their critical social media literacies through fact-checking. The study results show that knowledge gaps persist in truth manipulation on social media and the cognitional aspects in response to fabricated narratives. We emphasize the importance of further investigations in this domain.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".