Deciphering Social Behaviour: a Novel Biological Approach For Social Users Classification

TL;DR

This paper introduces a biological-inspired algorithm that classifies social media users as bots or genuine by analyzing behavioral similarities using DNA string comparison techniques, effectively identifying sophisticated bot accounts.

Contribution

It presents a novel classification method that applies biological DNA similarity algorithms to social media user behavior, improving detection of advanced social bots.

Findings

Successfully distinguishes between bots and genuine users.

Effectively identifies new groups of bots generated by Large Language Models.

Outperforms existing detection methods in complex scenarios.

Abstract

Social media platforms continue to struggle with the growing presence of social bots-automated accounts that can influence public opinion and facilitate the spread of disinformation. Over time, these social bots have advanced significantly, making them increasingly difficult to distinguish from genuine users. Recently, new groups of bots have emerged, utilizing Large Language Models to generate content for posting, further complicating detection efforts. This paper proposes a novel approach that uses algorithms to measure the similarity between DNA strings, commonly used in biological contexts, to classify social users as bots or not. Our approach begins by clustering social media users into distinct macro species based on the similarities (and differences) observed in their timelines. These macro species are subsequently classified as either bots or genuine users, using a novel metric we developed that evaluates their behavioral characteristics in a way that mirrors biological comparison methods. This study extends beyond past approaches that focus solely on identical behaviors via analyses of the accounts' timelines. By incorporating new metrics, our approach systematically classifies non-trivial accounts into appropriate categories, effectively peeling back layers to reveal non-obvious species.