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Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach
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 pub...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004046/ https://www.ncbi.nlm.nih.gov/pubmed/33804744 http://dx.doi.org/10.3390/s21062183 |
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author | Nguyen, Nhut-Lam Wang, Ming-Hung Dai, Yu-Chen Dow, Chyi-Ren |
author_facet | Nguyen, Nhut-Lam Wang, Ming-Hung Dai, Yu-Chen Dow, Chyi-Ren |
author_sort | Nguyen, Nhut-Lam |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8004046 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80040462021-03-28 Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach Nguyen, Nhut-Lam Wang, Ming-Hung Dai, Yu-Chen Dow, Chyi-Ren Sensors (Basel) Article 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. MDPI 2021-03-20 /pmc/articles/PMC8004046/ /pubmed/33804744 http://dx.doi.org/10.3390/s21062183 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nguyen, Nhut-Lam Wang, Ming-Hung Dai, Yu-Chen Dow, Chyi-Ren Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach |
title | Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach |
title_full | Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach |
title_fullStr | Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach |
title_full_unstemmed | Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach |
title_short | Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach |
title_sort | understanding malicious accounts in online political discussions: a multilayer network approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004046/ https://www.ncbi.nlm.nih.gov/pubmed/33804744 http://dx.doi.org/10.3390/s21062183 |
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