ReviewFundamental research2025
An overview of fake news detection: From a new perspective.
Review in Fundamental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development and popularization of Internet technology, the propagation and diffusion of information become much easier and faster. While making life more convenient, the Internet also promotes the wide spread of fake news, which will have a great negative impact on countries, societies, and individuals. Therefore, a lot of research efforts have been made to combat fake news. Fake news detection is typically a classification problem aiming at verifying the veracity of news contents, which may include texts, images and videos. This article provides a comprehensive survey of fake news detection. We first summarize three intrinsic characteristics of fake news by analyzing its entire diffusion process, namely intentional creation, heteromorphic transmission, and controversial reception. The first refers to why users publish fake news, the second denotes how fake news propagates and distributes, and the last means what viewpoints different users may hold for fake news. We then discuss existing fake news detection approaches according to these characteristics. Thus, this review will enable readers to better understand this field from a new perspective. We finally discuss the trends of technological advances in this field and also outline some potential directions for future research.
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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.