---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: D
      foaf_name: Assenmacher, D
      foaf_surname: Assenmacher
  - foaf_Person:
      foaf_givenName: L
      foaf_name: Clever, L
      foaf_surname: Clever
  - foaf_Person:
      foaf_givenName: JS
      foaf_name: Pohl, JS
      foaf_surname: Pohl
  - 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
  bibo_doi: 10.1007/978-3-030-49570-1_14
  dct_date: 2020^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-3-030-49570-1
  dct_language: eng
  dct_publisher: Springer International Publishing@
  dct_title: A Two-Phase Framework for Detecting Manipulation Campaigns in Social
    Media@
...