Beyond Words: Stylometric Analysis for Detecting AI Manipulation on Social Media

Recently, there has been a noticeable growth in textual content generated through advanced language models, such as chatGPT, across various social networks. ChatGPT can produce content that closely emulates human writing, making it indistinguishable from human content and introducing concerns regarding its potential exploitation by social bots for malicious purposes. This study undertakes a comprehensive investigation leveraging stylometric features to assess and identify bot accounts and chatGPT writing style on the Twitter platform. In particular, we extract stylometric features from bot- and human-written tweets, perform statistical tests, and evaluate the performance of machine-learning models fed by stylistic indicators. Our findings indicate that chatGPT-driven accounts are statistically different from human accounts based on consistency in their writing style, while the experimented models achieve an accuracy of up to 96% and 91% in the detection of chatGPT-based bot accounts and chatGPT-generated tweets, respectively. Finally, we assess the detection performance when adversarial text is introduced in test samples, demonstrating the robustness of the stylometry-based approach under adversarial attacks.