Towards Real-Time and Unsupervised Campaign Detection in Social Media
Assenmacher D, Adam L, Trautmann H, Grimme C
Abstract
The detection of orchestrated and potentially manipulative campaigns in social media is far more meaningful than analyzing single account behaviour but also more challenging in terms of pattern recognition, data processing, and computational complexity. While supervised learning methods need an enormous amount of reliable ground truth data to find rather inflexible patterns, classical unsupervised learning techniques need a lot of computational power to handle large amount of data. This makes them infeasible for real-time analysis. In this work, we demonstrate the applicability of text stream clustering for the real-time detection of coordinated campaigns.