Semantic Text Analysis for Detection of Compromised Accounts on Social Networks

Dominic Seyler, Lunan Li, Cheng Xiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Compromised accounts on social networks are regular user accounts that have been taken over by an entity with malicious intent. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel general framework for semantic analysis of text messages coming out from an account to detect compromised accounts. Our framework is built on the observation that normal users will use language that is measurably different from the language that an adversary would use when the account is compromised. We propose to use the difference of language models of users and adversaries to define novel interpretable semantic features for measuring semantic incoherence in a message stream. We study the effectiveness of the proposed semantic features using a Twitter data set. Evaluation results show that the proposed framework is effective for discovering compromised accounts on social networks and a KL-divergence-based language model feature works best.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
EditorsMartin Atzmuller, Michele Coscia, Rokia Missaoui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages417-424
Number of pages8
ISBN (Electronic)9781728110561
DOIs
StatePublished - Dec 7 2020
Event12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands
Duration: Dec 7 2020Dec 10 2020

Publication series

NameProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
CountryNetherlands
CityVirtual, Online
Period12/7/2012/10/20

Keywords

  • compromised accounts
  • incoherence detection
  • semantic analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Social Psychology
  • Communication

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