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...

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Autores principales: Assenmacher, Dennis, Clever, Lena, Pohl, Janina Susanne, Trautmann, Heike, Grimme, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
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.
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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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