Evidence map›Paper›PMID 35574264›Full record

ArticleInternational journal of data science and analytics2022

Online information disorder: fake news, bots and trolls.

Anastasia Giachanou, Xiuzhen Zhang, Alberto Barrón-Cedeño, Olessia Koltsova, Paolo Rosso

Abstract readEditorial
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of data science and analytics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Anastasia GiachanouMethodology and Statistics, Utrecht University, Utrecht, The Netherlands.
Xiuzhen ZhangSchool of Computing Technologies, RMIT University, Melbourne, Australia.
Alberto Barrón-CedeñoUniversità di Bologna, Bologna, Italy.
Olessia KoltsovaNational Research University Higher School of Economics, Moscow, Russia.
Paolo RossoPRHLT, Universitat Politècnica de València, Valencia, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent years have seen a tremendous increase in the propagation of different types of misinformation and disinformation, including among others fake news, rumours, clickbait and conspiracy theories. Misinformation involved in satire and clickbait, among others, has a different intention from disinformation. Despite many attempts by the research community, the development of technology to assist experts in detecting disinformation remains an open problem due to a number of challenges. For example, there are various biases such as confirmation bias and peer pressure that hinder users from recognizing non-credible information. In addition, fake news is intentionally written to confuse the readers, often containing a mixture of false and real information. To this end, in this editorial we present current challenges in the area of fake news identification and discuss contributions published in our special issue.

Indexed as

Fake newsFake news spreadersInformation disorder

Identifiers

PMID35574264
PMCPMC9080975

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.