Abstract
Trolling behavior is not simply a result of 'bad actors', an individual trait, or a linguistic phenomenon, but emerges from complex contagious social dynamics. This study uses formal concepts from information theory and complexity science to study it as such. The data comprised over 13 million Reddit comments, which were classified as troll or non-troll messages using the BERT model, fine-tuned with a human coding set. We derive the unique, minimally complex, and maximally predictive model from statistical mechanics, i.e., ε-machines and transducers, and can distinguish which aspects of trolling behaviors are both self-motivated and socially induced. While the vast majority of self-driven dynamics are like flipping a coin (86.3%), when social contagion is considered, most users (95.6%) show complex hidden multiple-state patterns. Within this complexity, trolling follows predictable transitions, with, for example, a 76% probability of remaining in a trolling state once it is reached. We find that replying to a trolling comment significantly increases the likelihood of switching to a trolling state or staying in it (72%). Besides being a showcase for the use of information-theoretic measures from dynamic systems theory to conceptualize human dynamics, our findings suggest that users and platform designers should go beyond calling out and removing trolls, but foster and design environments that discourage the dynamics leading to the emergence of trolling behavior.