Extracting social structure from darkweb forums

This paper explores various Social Network Analysis (SNA) techniques in order to identify a range of potentially ‘important’ members of Islamic Networks within Dark Web Forums. For this experiment, we conducted our investigation on five forums collected in previous work as part of the DarkWeb Forum...

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Main Authors: Phillips, E, Nurse, J, Goldsmith, M, Creese, S
Format: Conference item
Published: IARIA 2015
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author Phillips, E
Nurse, J
Goldsmith, M
Creese, S
author_facet Phillips, E
Nurse, J
Goldsmith, M
Creese, S
author_sort Phillips, E
collection OXFORD
description This paper explores various Social Network Analysis (SNA) techniques in order to identify a range of potentially ‘important’ members of Islamic Networks within Dark Web Forums. For this experiment, we conducted our investigation on five forums collected in previous work as part of the DarkWeb Forum portal and built upon the tool support created in our previous research in order to visualise and analyse the network. Whilst existing work attempts to identify these structures through state-of-the-art Computational Linguistic techniques, our work relies on the communication metadata alone. Our analysis involved first calculating a range of SNA metrics to better understand the group members, and then apply unsupervised learning in order to create clusters that would help classify the Dark Web Forums users into hierarchical clusters. In order to create our social networks, we investigated the effect of repeated author resolution and various weighting schemes on the ranking of forum members by creating four social networks per forum and evaluating the correlation of the top n users (for n = 10; 20; 30; 40; 50 and 100). Our results identified that varying the weighting schemes created more consistent ranking schemes than varying the repeated author resolution.
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spelling oxford-uuid:40118c74-bcca-4120-896b-8995b14c26932022-03-26T14:35:50ZExtracting social structure from darkweb forumsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:40118c74-bcca-4120-896b-8995b14c2693Symplectic Elements at OxfordIARIA2015Phillips, ENurse, JGoldsmith, MCreese, SThis paper explores various Social Network Analysis (SNA) techniques in order to identify a range of potentially ‘important’ members of Islamic Networks within Dark Web Forums. For this experiment, we conducted our investigation on five forums collected in previous work as part of the DarkWeb Forum portal and built upon the tool support created in our previous research in order to visualise and analyse the network. Whilst existing work attempts to identify these structures through state-of-the-art Computational Linguistic techniques, our work relies on the communication metadata alone. Our analysis involved first calculating a range of SNA metrics to better understand the group members, and then apply unsupervised learning in order to create clusters that would help classify the Dark Web Forums users into hierarchical clusters. In order to create our social networks, we investigated the effect of repeated author resolution and various weighting schemes on the ranking of forum members by creating four social networks per forum and evaluating the correlation of the top n users (for n = 10; 20; 30; 40; 50 and 100). Our results identified that varying the weighting schemes created more consistent ranking schemes than varying the repeated author resolution.
spellingShingle Phillips, E
Nurse, J
Goldsmith, M
Creese, S
Extracting social structure from darkweb forums
title Extracting social structure from darkweb forums
title_full Extracting social structure from darkweb forums
title_fullStr Extracting social structure from darkweb forums
title_full_unstemmed Extracting social structure from darkweb forums
title_short Extracting social structure from darkweb forums
title_sort extracting social structure from darkweb forums
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AT nursej extractingsocialstructurefromdarkwebforums
AT goldsmithm extractingsocialstructurefromdarkwebforums
AT creeses extractingsocialstructurefromdarkwebforums