---
res:
  bibo_abstract:
  - The detection of orchestrated and potentially manipulative campaigns in social
    media is far more meaningful than an- alyzing single account behaviour but also
    more challenging in terms of pattern recognition, data processing, and com- putational
    complexity. While supervised learning methods need an enormous amount of reliable
    ground truth data to find rather inflexible patterns, classical unsupervised learn-
    ing 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 coordi-
    nated campaigns.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: D
      foaf_name: Assenmacher, D
      foaf_surname: Assenmacher
  - foaf_Person:
      foaf_givenName: L
      foaf_name: Adam, L
      foaf_surname: Adam
  - foaf_Person:
      foaf_givenName: Heike
      foaf_name: Trautmann, Heike
      foaf_surname: Trautmann
      foaf_workInfoHomepage: http://www.librecat.org/personId=100740
    orcid: 0000-0002-9788-8282
  - foaf_Person:
      foaf_givenName: C
      foaf_name: Grimme, C
      foaf_surname: Grimme
  dct_date: 2020^xs_gYear
  dct_language: eng
  dct_title: Towards Real-Time and Unsupervised Campaign Detection in Social Media@
...