ArticleSN computer science2022
Fake Information Analysis and Detection on Pandemic in Twitter.
Article in SN computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Twitter has become a popular platform to receive daily updates. The more the people rely on it, the more critical it becomes to get genuine information out. False information can easily be shared on Twitter, which influences people's feelings, especially if fake information is linked to COVID-19. Therefore, it is of utmost importance to detect fake information before it becomes uncontrollable. Real-time tweets were used as part of this study. A few features like tweet's text, sentiment etc., were extracted and analyzed. The project returns a set of statistics determining the tweet's veracity. In this study, various classifiers have been used to see which of them works best with the proposed model in classifying the used dataset. The proposed model achieved the best accuracy of 84.54% and the highest F1-score of 0.842 with Random Forest. With careful analysis while feature selection and using few features, the model developed is equivalent in performance to the other models that use a lot of features. This confirms that the model developed is less complex and highly dependable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.