From Online Behaviours to Images: A Novel Approach to Social Bot Detection

TL;DR

This paper introduces a novel Twitter bot detection method that converts account actions into images and applies CNNs for classification, achieving competitive results with existing state-of-the-art techniques.

Contribution

The paper presents a new algorithm transforming social account actions into images and employs CNNs for bot detection, offering a novel approach in the field.

Findings

Detection performance comparable to state-of-the-art methods

Effective transformation of action sequences into images

CNN-based classification achieves high accuracy

Abstract

Online Social Networks have revolutionized how we consume and share information, but they have also led to a proliferation of content not always reliable and accurate. One particular type of social accounts is known to promote unreputable content, hyperpartisan, and propagandistic information. They are automated accounts, commonly called bots. Focusing on Twitter accounts, we propose a novel approach to bot detection: we first propose a new algorithm that transforms the sequence of actions that an account performs into an image; then, we leverage the strength of Convolutional Neural Networks to proceed with image classification. We compare our performances with state-of-the-art results for bot detection on genuine accounts / bot accounts datasets well known in the literature. The results confirm the effectiveness of the proposal, because the detection capability is on par with the state of the art, if not better in some cases.