Spotting Fake Profiles in Social Networks via Keystroke Dynamics

Abstract: Spotting and removing fake profiles could curb the menace of fake news in society. This paper, thus, investigates fake profile detection in social networks via users' typing patterns. We created a novel dataset of 468 posts from 26 users on three social networks: Facebook, Instagram, and X (previously Twitter) over six sessions. Then, we extract a series of features from keystroke timings and use them to predict whether two posts originated from the same users using three prominent statistical methods and their score-level fusion. The models' performance is evaluated under same, cross, and combined-cross-platform scenarios. We report the performance using k-rank accuracy for k varying from 1 to 5. The best-performing model obtained accuracies between 91.6-100% on Facebook (Fusion), 70.8-87.5% on Instagram (Fusion), and 75-87.5% on X (Fusion) for k from 1 to 5. Under a cross-platform scenario, the fusion model achieved mean accuracies of 79.1-91.6%, 87.5-91.6%, and 83.3-87.5% when trained on Facebook, Instagram, and Twitter posts, respectively. In combined cross-platform, which involved mixing two platforms' data for model training while testing happened on the third platform's data, the best model achieved accuracy ranges of 75-95.8% across different scenarios. The results highlight the potential of the presented method in uncovering fake profiles across social network platforms.