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Research on Online Digital Cultures-Community Extraction and Analysis by Markov and k-Means Clustering

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Original languageEnglish
Title of host publicationPersonal Analytics and Privacy
Subtitle of host publicationAn Individual and Collective Perspective - 1st International Workshop, PAP 2017, Held in Conjunction with ECML PKDD 2017, Revised Selected Papers
PublisherSpringer Verlag
Pages110-121
Number of pages11
Volume10708
ISBN (Electronic)978-3-319-71970-2
ISBN (Print)978-3-319-71969-6
DOIs
Published18 Sep 2017
Event1st International Workshop on Personal Analytics and Privacy, PAP 2017, Held in Conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 - Skopje, Macedonia, The Former Yugoslav Republic of
Duration: 18 Sep 201718 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10708 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Personal Analytics and Privacy, PAP 2017, Held in Conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
Country/TerritoryMacedonia, The Former Yugoslav Republic of
CitySkopje
Period18/09/201718/09/2017

King's Authors

Abstract

We investigate approaches to personal data analytics that involves the participation of all actors in our shared digital culture. We analyse their communities by identifying and clustering social relations using mobile and social media data. The work is part of our effort to develop tools to create a “social data commons”, an open research environment that will share innovative tools and data sets to researchers interested in accessing the data that surrounds the production and circulation of digital culture and their actors. This experiment focuses on the groups of clustered relations that are formed within a user’s social data traces. Community extraction is a popular part of the analysis of social data. We have applied the technique of Markov Clustering to the Twitter networks of social actors. Qualitatively, we demonstrate that it is more effective than the Louvain method for finding social groups known to the subjects, while still being very simple to implement. We also demonstrate that traces of cell towers captured using our “MobileMiner” mobile application are sufficient to capture significant details about their social relations by the simple application of k-means.

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