Human, AI, and Hybrid Ensembles for Detection of Adaptive, RL-based Social Bots

Abstract

The use of reinforcement learning to dynamically adapt and evade detection is now well-documented in several cybersecurity settings including Covert Social Influence Operations (CSIOs), in which bots try to spread disinformation. While AI bot detectors have improved greatly, they are largely limited to detecting static bots that do not adapt dynamically. We present the first systematic study comparing the ability of humans, AI models, and hybrid Human-AI ensembles in detecting adaptive bots powered by reinforcement learning (RL) . Using data from a controlled, IRB-approved, five-day experiment with participants interacting on a social media platform infiltrated by RL-trained bots spreading disinformation to influence participants on 4 topics, we examine factors potentially shaping human detection capabilities: demographic characteristics, temporal learning effects, social network position, engagement patterns, and collective intelligence mechanisms. We first test 13 hypotheses comparing human bot detection performance against 6 state-of-the-art AI bot detectors — 2 that use traditional machine learning and 4 that use large language models. We investigate several aggregation strategies that combine human reports of bots with AI predictions, as well as retraining protocols that leverage human supervision. Our findings challenge intuitive assumptions about bot detection, reveal unexpected patterns in how humans identify bots, and show that combining human bot reports with AI predictions outperforms humans alone and AI alone. We conclude with a discussion of the practical implications of these results for industry.