Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan.
Sensors (Basel). 2021 Mar 20;21(6):2183. doi: 10.3390/s21062183.
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.
在线社交媒体平台在政治传播中发挥着重要作用，用户可以在其中自由表达和交流政治观点。政治实体利用社交媒体平台作为传播信息、与选民互动甚至影响舆论的重要渠道。为此，一些组织可能会创建一个或多个账户来参与在线政治讨论。利用这些账户，他们可以宣传候选人，攻击竞争对手。为了避免这种误导性言论，提高网络社会的透明度，发现这些恶意账户并了解其行为是至关重要的问题。在本文中，我们旨在使用基于网络的分析来感知试图在政治讨论论坛上操纵公众舆论的有影响力的人工恶意账户。为此，我们从 2018 年 5 月 25 日至 2020 年 1 月 11 日（选举日），从著名的台湾讨论论坛上收集了与选举相关的文章和恶意账户。我们将讨论网络建模为一个多层网络，并使用各种中心性度量来感知不仅在单层网络中，而且在网络的不同层中都具有影响力的恶意账户。此外，还对社区进行了分析，以发现每个网络层的突出社区及其特征。结果表明，我们提出的方法可以成功识别出几个有影响力的恶意账户和具有明显行为差异的突出社区。
