A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media
Assenmacher D, Clever L, Pohl JS, Trautmann H, Grimme C
Abstract
The identification of coordinated campaigns within Social Media is a complex task that is often hindered by missing labels and large amounts of data that have to be processed. We propose a new two-phase framework that uses unsupervised stream clustering for detecting suspicious trends over time in a first step. Afterwards, traditional offline analyses are applied to distinguish between normal trend evolution and malicious manipulation attempts. We demonstrate the applicability of our framework in the context of the final days of the Brexit in 2019/2020.
Keywords
Social campaign detection; Stream clustering; Unsupervised learning