Towards Real-Time and Unsupervised Campaign Detection in Social Media

D Assenmacher, L Adam, H Trautmann, C Grimme

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of the Florida Artificial Intelligence Research Society Conference
Publication statusPublished - 2020
Externally publishedYes
Event33rd International FLAIRS Conference, FLAIRS 2020 - Virtual Event
Duration: 17 May 202020 May 2020
Conference number: 33

Conference

Conference33rd International FLAIRS Conference, FLAIRS 2020
Abbreviated titleFLAIRS 2020
CityVirtual Event
Period17/05/2020/05/20

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