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.

Social network platforms have become popular channels for election campaigns and political propaganda in recent years. However, some entities may employ a group of accounts to generate and shape public opinions. This study investigated the publication and commenting activities by collecting a 6-month-long user behavior data on the most extensively used online forum in Taiwan during a local election in 2018. A series of comparative studies between normal and verified malicious accounts are conducted. From the results, we find malicious authors published articles with more comments and received polarized ratings from online users. CCS CONCEPTS • Networks → Social media networks; Online social networks.