Discovering Coordinated Processes From Social Online Networks

TL;DR

This paper introduces a novel approach using process mining techniques to uncover and analyze coordinated behaviors of AI and human agents in social online networks, specifically applying it to Twitter data.

Contribution

It pioneers the application of process mining to social network data for detecting coordinated behaviors and distinguishing between AI and human activities.

Findings

Process models reveal coordinated behaviors in social networks.

Structural properties differentiate AI-driven from human-driven activities.

Method effectively analyzes real-world Twitter data.

Abstract

The rapid growth of social media presents a unique opportunity to study coordinated agent behavior in an unfiltered environment. Online processes often exhibit complex structures that reflect the nature of the user behavior, whether it is authentic and genuine, or part of a coordinated effort by malicious agents to spread misinformation and disinformation. Detection of AI-generated content can be extremely challenging due to the high quality of large language model-generated text. Therefore, approaches that use metadata like post timings are required to effectively detect coordinated AI-driven campaigns. Existing work that models the spread of information online is limited in its ability to represent different control flows that occur within the network in practice. Process mining offers techniques for the discovery of process models with different routing constructs and are yet to be applied to social networks. We propose to leverage process mining methods for the discovery of AI and human agent behavior within social networks. Applying process mining techniques to real-world Twitter (now X) event data, we demonstrate how the structural and behavioral properties of discovered process models can reveal coordinated AI and human behaviors online.