Selective tweeting of COVID-19 articles: Does title or abstract positivity influence dissemination?
Bibliographic record
Abstract
Abstract Background Previous research has shown that articles may be cited more frequently on the basis of title or abstract positivity. Whether a similar selective sharing practice exists on Twitter is not well understood. The objective of this study was to assess if COVID-19 articles with positive titles or abstracts were tweeted more frequently than those with non-positive titles or abstracts. Methods COVID-19 related articles published between January 1 st and April 14 th , 2020 were extracted from the LitCovid database and all articles were screened for eligibility. Titles and abstracts were classified using a list of positive and negative words from a previous study. A negative binomial regression analysis controlling for confounding variables (2018 impact factor, open access status, continent of the corresponding author, and topic) was performed to obtain regression coefficients, with the p values obtained by likelihood ratio testing. Results A total of 3752 COVID-19 articles were included. Of the included studies, 44 titles and 112 abstracts were positive; 1 title and 7 abstracts were negative; and 3707 titles and 627 abstracts were neutral. Articles with positive titles had a lower tweet rate relative to articles with non-positive titles, with a regression coefficient of -1.10 (P < .001), while the positivity of the abstract did not impact tweet rate (P = .2218). Conclusion COVID-19 articles with non-positive titles are preferentially tweeted, while abstract positivity does not influence tweet rate.
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 | 0.040 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".