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dc.contributor.authorEfstathiades, Haritonen
dc.contributor.authorAntoniades, Demetrisen
dc.contributor.authorPallis, George C.en
dc.contributor.authorDikaiakos, Marios D.en
dc.contributor.editorPei J.en
dc.contributor.editorTang J.en
dc.contributor.editorSilvestri F.en
dc.creatorEfstathiades, Haritonen
dc.creatorAntoniades, Demetrisen
dc.creatorPallis, George C.en
dc.creatorDikaiakos, Marios D.en
dc.description.abstractUbiquitous Internet connectivity enables users to update their Online Social Network profile from any location and at any point in time. These, often geo-tagged, data can be used to provide valuable information to closely located users, both in real time and in aggregated form. However, despite the fact that users publish geo-tagged information, only a small number implicitly reports their base location in their Online Social Network profile. In this paper we present a simple yet effective methodology for identifying a user's key locations, namely her home and work places. We evaluate our methodology with Twitter datasets collected from the country of Netherlands, city of London and Los Angeles county. Furthermore, we combine Twitter and LinkedIn information to construct a work location dataset and evaluate our methodology. Results show that our proposed methodology not only outperforms state-of-the-art methods by at least 30% in terms of accuracy, but also cuts the detection radius at least at half the distance from other methods. © 2015 ACM.en
dc.publisherAssociation for Computing Machinery, Incen
dc.sourceProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015en
dc.sourceIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015en
dc.subjectReal timeen
dc.subjectSocial networking (online)en
dc.subjectOn-line social networksen
dc.subjectInternet connectivityen
dc.subjectKey locationen
dc.subjectLos angelesen
dc.subjectState-of-the-art methodsen
dc.subjectWork placeen
dc.titleIdentification of key locations based on online social network activityen
dc.description.endingpage225 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: Association for Computing Machinery SIGKDD (ACM SIGKDD)en
dc.description.noteset al.en
dc.description.notesIEEE Computer Societyen
dc.description.notesIEEE TCDEen
dc.description.notesConference code: 117441en
dc.description.notesCited By :4</p>en
dc.contributor.orcidPallis, George C. [0000-0003-1815-5468]
dc.contributor.orcidDikaiakos, Marios D. [0000-0002-4350-6058]

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