Evaluating the effectiveness of publishers' features in fake news detection on social media.

Abstract readNews

In one paragraph

Article in Multimedia tools and applications, 2023. 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.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

Online social networks for prevention and promotion of oral health: a systematic review.Canadian journal of dental hygiene : CJDH = Journal canadien de l'hygiene dentaire : JCHD · 2023
Pooled it

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

2 authors.

Ali JarrahiElectrical and Computer Engineering, University of Zanjan, Zanjan, Iran.ORCID 0000-0002-4969-3624

Leila SafariElectrical and Computer Engineering, University of Zanjan, Zanjan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the expansion of the Internet and attractive social media infrastructures, people prefer to follow the news through these media. Despite the many advantages of these media in the news field, the lack of control and verification mechanism has led to the spread of fake news as one of the most critical threats to democracy, economy, journalism, health, and freedom of expression. So, designing and using efficient automated methods to detect fake news on social media has become a significant challenge. One of the most relevant entities in determining the authenticity of a news statement on social media is its publishers. This paper examines the publishers' features in detecting fake news on social media, including

Indexed as

CreditRank algorithmDeep neural networkFake news detectionMachine learningSocial mediaText classification

Identifiers

PMID35431607

DOI10.1007/s11042-022-12668-8

PMCPMC8995145

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.