Evidence map›Paper›PMID 35789600›Full record

ArticleJournal of ambient intelligence and humanized computing2022

A model to measure the spread power of rumors.

Zoleikha Jahanbakhsh-Nagadeh, Mohammad-Reza Feizi-Derakhshi, Majid Ramezani, Taymaz Akan, Meysam Asgari-Chenaghlu, Narjes Nikzad-Khasmakhi, Ali-Reza Feizi-Derakhshi, Mehrdad Ranjbar-Khadivi, Elnaz Zafarani-Moattar, Mohammad-Ali Balafar

Abstract read
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In one paragraph

Article in Journal of ambient intelligence and humanized computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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. Fake news detection: deep semantic representation with enhanced feature engineering.International journal of data science and analytics · 2023
    Article
  2. A model to measure the spread power of rumors.Journal of ambient intelligence and humanized computing · 2022
    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

10 authors.

Zoleikha Jahanbakhsh-NagadehComputerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
Mohammad-Reza Feizi-DerakhshiComputerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran.ORCID 0000-0002-8548-976X
Majid RamezaniComputerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
Taymaz AkanComputerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
Meysam Asgari-ChenaghluComputerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
Narjes Nikzad-KhasmakhiComputerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
Ali-Reza Feizi-DerakhshiComputerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
Mehrdad Ranjbar-KhadiviComputerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
Elnaz Zafarani-MoattarComputerized Intelligence Systems Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
Mohammad-Ali BalafarDepartment of Computer Engineering, University of Tabriz, Tabriz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories "importance" (28 features) and "ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors.

Indexed as

Ambiguity of rumorAutomatic rumor verificationImportance of rumorSpread Power of Rumor (SPR)

Identifiers

PMID35789600
PMCPMC9244448

What OpenQuestion holds

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Registered trials

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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.