Lured by Likes: Evaluating the Online Visibility of Predatory Journals through Altmetric Indicators
Bibliographic record
Abstract
The purpose of this study is to evaluate the influence of questionable journals on social media. Now defunct and severely western-biased Beall’s list was used to identify predatory journals mostly from the non-western countries, and Altmetric Explorer was used to extract the social media attention. The results showed that Beall’s list had 1,310 predatory journals as of March 2025, and 7,873 articles from 77 deceptive journals garnered web attention from various social media platforms, with a total Altmetric Attention Score (AAS) of 37,427. The low intake of predatory articles on social media can be considered as a parameter in identifying the deceptive journals. Predatory journals were present on 18 different platforms, with a higher presence on Mendeley, accounting for 234467 (88.13%) mentions, and Twitter, with 20949 (7.87%) mentions. The articles from the journal "Aging" received the highest social attention, with 54,748 mentions for its 4,720 articles. The geographical results showed that web discussions about the questionable articles were predominantly from English-speaking countries, including the United States, the United Kingdom, and Canada. Finally, the study reported a significant, weak positive correlation between Dimensions citation (DC) and altmetric attention score, with a correlation coefficient value of 0.23 (rho = 0.23, p ≤ 0.001) for the articles. The present study offers valuable insights for the entire research community on utilising altmetrics as a reliable indicator for identifying predatory journals.
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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".