ArticleAnnals of operations research2022
Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions.
Article in Annals of operations research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 155 citations in OpenAlex.
- The role of AI in combating misinformation: leveraging text mining and social networking analysis.Scientific reports · 2026Article
- Fake news research trends, linkages to generative artificial intelligence and sustainable development goals.Heliyon · 2024Article
- Management of drug supply chain information based on "artificial intelligence + vendor managed inventory" in China: perspective based on a case study.Frontiers in pharmacology · 2024Article
- Impact of strategies to mitigate misinformation in diverse settings and populations: a protocol for a living evidence synthesis.BMJ open · 2023Article
- Artificial intelligence applied to potential assessment and talent identification in an organisational context.Heliyon · 2023Article
- Digital economy and green transformation of regional industries: New insights from sustainability.Science progressArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 6 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.
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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.