Early Detection of Pathogenic Social Media Accounts

Hamidreza Alvari, Elham Shaabani, Paulo Shakarian

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter introduces a time-decay causality metric and incorporates it into a causal community detection-based algorithm to identify PSMs within a short time frame around their activity. The proposed algorithm is applied to groups of accounts sharing similar causality features and is followed by a classification algorithm to classify accounts as PSM or not. Unlike existing techniques that take significant time to collect information such as network, cascade path, or content, our scheme relies solely on action log of users. Results on the ISIS-B dataset described previously, demonstrate effectiveness and efficiency of our approach. We achieved precision of 0.84 for detecting PSMs only based on their first 10 days of activity; the misclassified accounts were then detected 10 days later.

Original languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages39-49
Number of pages11
DOIs
StatePublished - 2021

Publication series

NameSpringerBriefs in Computer Science
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776

ASJC Scopus subject areas

  • Computer Science(all)

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