@inproceedings{6837636e77cb45ff9a9d9e2c3f738cf2,
title = "Detecting pathogenic social media accounts without content or network structure",
abstract = "The spread of harmful mis-information in social media is a pressing problem. We refer accounts that have the capability of spreading such information to viral proportions as 'Pathogenic Social Media' accounts. These accounts include terrorist supporters accounts, water armies, and fake news writers. We introduce an unsupervised causality-based framework that also leverages label propagation. This approach identifies these users without using network structure, cascade path information, content and user's information. We show our approach obtains higher precision (0.75) in identifying Pathogenic Social Media accounts in comparison with random (precision of 0.11) and existing bot detection (precision of 0.16) methods.",
keywords = "Causality, Pathogenic Social Media, Social Bot, Terrorist Accounts, Water Armies",
author = "Elham Shaabani and Ruocheng Guo and Paulo Shakarian",
note = "Funding Information: Some of the authors are supported through the DoD Minerva program and AFOSR (grant FA9550-15-1-0159). Publisher Copyright: {\textcopyright} 2018 IEEE.; 1st International Conference on Data Intelligence and Security, ICDIS 2018 ; Conference date: 08-04-2018 Through 10-04-2018",
year = "2018",
month = may,
day = "25",
doi = "10.1109/ICDIS.2018.00016",
language = "English (US)",
series = "Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "57--64",
booktitle = "Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018",
}