Misinformation Detection and Adversarial Attack Cost Analysis in Directional Social Networks

Huajie Shao, Shuochao Yao, Andong Jing, Shengzhong Liu, Dongxin Liu, Tianshi Wang, Jinyang Li, Chaoqi Yang, Ruijie Wang, Tarek Abdelzaher

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

Abstract

This paper develops a novel detection system of possibly fake accounts on public social media, called FADE, that uses features based on group behaviors to identify suspicious groups. The work is motivated by the prospect of mitigating misinformation campaigns on social media, where malicious entities on directional social networks pose as credible sources and coordinate the spreading of highly corroborated false information. Instead of account-level detection, this paper aims to detect the very group activity that underlies misinformation campaigns; namely, the coordinated spreading of messages to boost (misinformation) visibility. The existing group detection methods group users into two clusters (fake or not) and directly produce clusters of fake accounts. Conversely, we group users into many clusters based on information propagation patterns and user features and then classify them. The benefit of multiple clusters is that we can detect suspicious behavior more easily from cluster-wide statistics. In order to improve clustering accuracy, we analyze and select the most important features for clustering based on Bayesian optimization instead of using all the features. Accordingly, similarity metrics are defined that allow clustering of individually plausible accounts in a manner that enables one to detect suspicious clusters of activity. Cluster-level features are then used to decide if the cluster is benign. We further explore the cost of adversarial attacks on our detection model. Evaluation results on Twitter data sets demonstrate that our proposed approach outperforms state-of-the-art baselines in detecting accounts created for information manipulation campaigns. In addition, we show that the cost of subverting detection (without reducing the effectiveness of the attacker's campaign) is high.

Original languageEnglish (US)
Title of host publicationICCCN 2020 - 29th International Conference on Computer Communications and Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728166070
DOIs
StatePublished - Aug 2020
Event29th International Conference on Computer Communications and Networks, ICCCN 2020 - Honolulu, United States
Duration: Aug 3 2020Aug 6 2020

Publication series

NameProceedings - International Conference on Computer Communications and Networks, ICCCN
Volume2020-August
ISSN (Print)1095-2055

Conference

Conference29th International Conference on Computer Communications and Networks, ICCCN 2020
CountryUnited States
CityHonolulu
Period8/3/208/6/20

Keywords

  • adversarial attack
  • clustering
  • fake accounts detection
  • similarity
  • social networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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