DNA-inspired online behavioral modeling and its application to spambot detection

TL;DR

This paper introduces a novel DNA-inspired method for modeling online user behavior, successfully applied to distinguish genuine users from spambots on Twitter, demonstrating platform-agnostic potential.

Contribution

It is the first to adapt DNA-inspired analysis techniques for online behavioral modeling and spambot detection, offering a simple yet effective approach.

Findings

Effective discrimination between genuine and spambot accounts

DNA-inspired features provide compact user behavior characterization

Method is platform and technology agnostic

Abstract

We propose a strikingly novel, simple, and effective approach to model online user behavior: we extract and analyze digital DNA sequences from user online actions and we use Twitter as a benchmark to test our proposal. We obtain an incisive and compact DNA-inspired characterization of user actions. Then, we apply standard DNA analysis techniques to discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports our proposal, showing its effectiveness and viability. To the best of our knowledge, we are the first ones to identify and adapt DNA-inspired techniques to online user behavioral modeling. While Twitter spambot detection is a specific use case on a specific social media, our proposed methodology is platform and technology agnostic, hence paving the way for diverse behavioral characterization tasks.