Evidence map›Paper›PMID 38066201›Full record

ArticleScientific reports2023

A structural approach to detecting opinion leaders in Twitter by random matrix theory.

Saeedeh Mohammadi, Parham Moradi, Andrey Trufanov, G Reza Jafari

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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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

4 authors.

Saeedeh MohammadiPhysics Department, Shahid Beheshti University, Tehran, 1983969411, Iran.
Parham MoradiPhysics Department, Shahid Beheshti University, Tehran, 1983969411, Iran.
Andrey TrufanovInstitute of Information Technology and Data Science, Irkutsk National Research Technical University, Irkutsk, Russia.
G Reza JafariPhysics Department, Shahid Beheshti University, Tehran, 1983969411, Iran. gjafari@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a novel approach leveraging Random Matrix Theory (RMT) to identify influential users and uncover the underlying dynamics within social media discourse networks. Focusing on the retweet network associated with the 2021 Iranian presidential election, our study reveals intriguing findings. RMT analysis unveils that power dynamics within both poles of the network do not conform to a "one-to-many" pattern, highlighting a select group of users wielding significant influence within their clusters and across the entire network. By harnessing Random Matrix Theory (RMT) and complementary methodologies, we gain a profound understanding of the network's structure and, in turn, unveil the intricate dynamics of the discussion extending beyond mere structural analysis. In sum, our findings underscore the potential of RMT as a tool to gain deeper insights into network dynamics, particularly within popular discussions. This approach holds promise for investigating opinion leaders in diverse political and non-political dialogues.

Identifiers

PMID38066201
PMCPMC10709311

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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.