Spotting Fake Profiles in Social Networks via Keystroke Dynamics

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.
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