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A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347411/ http://dx.doi.org/10.1007/978-3-030-49570-1_14 |
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author | Assenmacher, Dennis Clever, Lena Pohl, Janina Susanne Trautmann, Heike Grimme, Christian |
author_facet | Assenmacher, Dennis Clever, Lena Pohl, Janina Susanne Trautmann, Heike Grimme, Christian |
author_sort | Assenmacher, Dennis |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7347411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73474112020-07-10 A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media Assenmacher, Dennis Clever, Lena Pohl, Janina Susanne Trautmann, Heike Grimme, Christian Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis Article 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. 2020-05-12 /pmc/articles/PMC7347411/ http://dx.doi.org/10.1007/978-3-030-49570-1_14 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Assenmacher, Dennis Clever, Lena Pohl, Janina Susanne Trautmann, Heike Grimme, Christian A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media |
title | A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media |
title_full | A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media |
title_fullStr | A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media |
title_full_unstemmed | A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media |
title_short | A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media |
title_sort | two-phase framework for detecting manipulation campaigns in social media |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347411/ http://dx.doi.org/10.1007/978-3-030-49570-1_14 |
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