A Proposal for Uncovering Hidden Social Bots via Genetic Similarity

TL;DR

This paper proposes a novel adaptive method using genetic similarity algorithms to classify social media users as bots or genuine, aiming to improve detection of evolving social bots, including those powered by large language models.

Contribution

It introduces a genetic similarity-based classification approach that clusters users into macro species and detects bots by analyzing behavioral evolution.

Findings

Initial clustering of users into macro species based on timeline similarities

Classification of macro species as bots or genuine using genetic characteristics

Potential to extend existing detection methods with new metrics

Abstract

Social media platforms face an ongoing challenge in combating the proliferation of social bots, automated accounts that are also known to distort public opinion and support the spread of disinformation. Over the years, social bots have evolved greatly, often becoming indistinguishable from real users, and more recently, families of bots have been identified that are powered by Large Language Models to produce content for posting. We suggest an idea to classify social users as bots or not using genetic similarity algorithms. These algorithms provide an adaptive method for analyzing user behavior, allowing for the continuous evolution of detection criteria in response to the ever-changing tactics of social bots. Our proposal involves an initial clustering of social users into distinct macro species based on the similarities of their timelines. Macro species are then classified as either bot or genuine based on genetic characteristics. The preliminary idea we present, once fully developed, will allow existing detection applications based on timeline equality alone to be extended to detect bots. By incorporating new metrics, our approach will systematically classify non-trivial accounts into appropriate categories, effectively peeling back layers to reveal non-obvious species.