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

Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi

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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.
Original languageEnglish
JournalI E E E Intelligent Systems
Issue number5
Pages (from-to)58-64
Publication statusPublished - 2016

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