ReviewSocial network analysis and mining2022
Applications of machine learning for COVID-19 misinformation: a systematic review.
Review in Social network analysis and mining, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Acute respiratory infections prediction through social media data and machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Article
- Analysis of child development facts and myths using text mining techniques and classification models.Heliyon · 2024Article
- Article
- Defining Misinformation and Related Terms in Health-Related Literature: Scoping Review.Journal of medical Internet research · 2023Article
- The moderating influence of perceived government information transparency on COVID-19 pandemic information adoption on social media systems.Frontiers in psychology · 2023Article
- A comparative analysis of the COVID-19 Infodemic in English and Chinese: insights from social media textual data.Frontiers in public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inflammable growth of misinformation on social media and other platforms during pandemic situations like COVID-19 can cause significant damage to the physical and mental stability of the people. To detect such misinformation, researchers have been applying various machine learning (ML) and deep learning (DL) techniques. The objective of this study is to systematically review, assess, and synthesize state-of-the-art research articles that have used different ML and DL techniques to detect COVID-19 misinformation. A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey was solely centered on reproducible and high-quality research. We reviewed 43 papers that fulfilled our inclusion criteria out of 260 articles found from our keyword search. We have surveyed a complete pipeline of COVID-19 misinformation detection. In particular, we have identified various COVID-19 misinformation datasets and reviewed different data processing, feature extraction, and classification techniques to detect COVID-19 misinformation. In the end, the challenges and limitations in detecting COVID-19 misinformation using ML techniques and the future research directions are discussed.
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.