A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media
Assenmacher D, Clever L, Pohl JS, Trautmann H, Grimme C
Research article in edited proceedings (conference) | Peer reviewedAbstract
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.
Details about the publication
Publisher: Meiselwitz G
Book title: Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis
Page range: 201-214
Publishing company: Springer Nature
Place of publication: Cham
Status: Published
Release year: 2020
Language in which the publication is written: English
Conference: International Conference on Human-Computer Interaction, Copenhagen, Denmark
ISBN: 978-3-030-49570-1
Keywords: Social campaign detection; Stream clustering; Unsupervised learning
Authors from the University of Münster
| Assenmacher, Dennis | Data Science: Statistics and Optimization (Statistik) |
| Clever, Lena | Data Science: Statistics and Optimization (Statistik) |
| Grimme, Christian | Data Science: Statistics and Optimization (Statistik) Research Group Computational Social Science and Systems Analysis (CSSSA) |
| Lütke-Stockdiek, Janina Susanne | Data Science: Statistics and Optimization (Statistik) Research Group Computational Social Science and Systems Analysis (CSSSA) |
| Trautmann, Heike | Data Science: Statistics and Optimization (Statistik) |