DNA-inspired online behavioral modeling and its application to spambot detection
This addresses spambot detection on social media, but the methodology is incremental as it adapts existing DNA analysis techniques to a new domain.
The authors tackled the problem of modeling online user behavior by extracting digital DNA sequences from user actions and applied it to spambot detection on Twitter, achieving effective results as supported by experiments.
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.