Evidence map›Paper›PMID 33804744›Full record

ArticleSensors (Basel, Switzerland)2021

Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach.

Nhut-Lam Nguyen, Ming-Hung Wang, Yu-Chen Dai, Chyi-Ren Dow

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Nhut-Lam NguyenDepartment of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan.ORCID 0000-0002-8623-8428
Ming-Hung WangDepartment of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan.ORCID 0000-0002-5680-4003
Yu-Chen DaiDepartment of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan.
Chyi-Ren DowDepartment of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan.ORCID 0000-0002-0978-9576

Funding

Ministry of Science and Technology, Taiwan MOST 107-2218-E-035-009-MY3
6 · The paper itself

Abstract

Online social media platforms play an important role in political communication where users can freely express and exchange their political opinion. Political entities have leveraged social media platforms as essential channels to disseminate information, interact with voters, and even influence public opinion. For this purpose, some organizations may create one or more accounts to join online political discussions. Using these accounts, they could promote candidates and attack competitors. To avoid such misleading speeches and improve the transparency of the online society, spotting such malicious accounts and understanding their behaviors are crucial issues. In this paper, we aim to use network-based analysis to sense influential human-operated malicious accounts who attempt to manipulate public opinion on political discussion forums. To this end, we collected the election-related articles and malicious accounts from the prominent Taiwan discussion forum spanning from 25 May 2018 to 11 January 2020 (the election day). We modeled the discussion network as a multilayer network and used various centrality measures to sense influential malicious accounts not only in a single-layer but also across different layers of the network. Moreover, community analysis was performed to discover prominent communities and their characteristics for each layer of the network. The results demonstrate that our proposed method can successfully identify several influential malicious accounts and prominent communities with apparent behavior differences from others.

Indexed as

Social MediaAttitudeCommunicationHumansPoliticsTaiwaninfluential usersinformation manipulationmalicious usersmultilayer networkpolitical propagandasocial media

Identifiers

PMID33804744
PMCPMC8004046

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.