A Bibliometric Analysis of Disinformation through Social Media
Bibliographic record
Abstract
The study’s purpose is to systematically review the scholarly literature about disinformation on social media, a space with enhanced concerns about nurturing propaganda and conspiracies. The systematic review methodology was applied to analyze 264 peer-reviewed articles published from 2010 to 2020, extracted from the Web of Science core collection database. Descriptive and bibliometric analysis techniques were used to document the findings. The analysis revealed an increase in the trend of publishing disinformation on social media and its impact on users’ cognitive responses from 2017 onwards. The USA appears to be the most influential node with its more significant role in advancing research on disinformation. The content analysis identified five psychosocial and political factors: influencing individual users’ perceptions, providing easy access to radicalism using personality profiles, social media use to influence political opinions, lack of critical social media literacies, and hoax flourish disinformation. Our research shows a knowledge gap in how disinformation directly shapes communal psychosocial narratives. We highlight the need for future research to explore and examine the antecedents, consequences, and impact of disinformation on social media and how it affects citizens’ cognition, critical thinking, and well-being.
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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 | 0.022 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.236 | 0.239 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".