DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection

A novel, simple, and effective approach to modeling online user behavior extracts and analyzes digital DNA sequences from user online actions and uses Twitter as a benchmark to test the proposal. Specifically, the model obtains an incisive and compact DNA-inspired characterization of user actions. T...

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Bibliographic Details
Published in:IEEE intelligent systems 2016-09, Vol.31 (5), p.58-64
Main Authors: Cresci, Stefano, Di Pietro, Roberto, Petrocchi, Marinella, Spognardi, Angelo, Tesconi, Maurizio
Format: Article
Language:English
Subjects:
Bioinformatics
Biological information theory
Data mining
Deoxyribonucleic acid
Digital media
DNA
Fingerprint recognition
Gene sequencing
intelligent systems
Knowledge management
knowledge representation formalisms and methods
Media
Modelling
Social networks
social science methods or tools
Social sciences
Twitter
User behavior
Viability
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Summary:A novel, simple, and effective approach to modeling online user behavior extracts and analyzes digital DNA sequences from user online actions and uses Twitter as a benchmark to test the proposal. Specifically, the model obtains an incisive and compact DNA-inspired characterization of user actions. Then, standard DNA analysis techniques discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports the proposal, showing its effectiveness and viability. Although Twitter spambot detection is a specific use case on a specific social media platform, the proposed methodology is platform and technology agnostic, paving the way for diverse behavioral characterization tasks.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2016.29