Show simple item record

dc.contributor.authorFrommholz, Ingoen
dc.contributor.authoral-Khateeb, Haideren
dc.contributor.authorPotthast, Martinen
dc.contributor.authorGhasem, Zinnaren
dc.contributor.authorShukla, Mitulen
dc.contributor.authorShort, Emmaen
dc.date.accessioned2019-01-28T11:36:42Z
dc.date.available2019-01-28T11:36:42Z
dc.date.issued2016-06-01
dc.identifier.citationFrommholz I, Al-Khateeb H, Potthast M, Ghasem Z, Shukla M, Short E (2016) 'On textual analysis and machine learning for cyberstalking detection', Datenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.V, 16 (2), pp.127-135.en
dc.identifier.issn1610-1995
dc.identifier.pmid29368749
dc.identifier.doi10.1007/s13222-016-0221-x
dc.identifier.urihttp://hdl.handle.net/10547/623097
dc.description.abstractCyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttps://link.springer.com/article/10.1007/s13222-016-0221-xen
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750836/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectcyber securityen
dc.subjectcyberstalkingen
dc.subjectcyber harassmenten
dc.subjecttext analyticsen
dc.subjectauthor identificationen
dc.subjectmachine learningen
dc.titleOn textual analysis and machine learning for cyberstalking detectionen
dc.typeArticleen
dc.contributor.departmentUniversity of Bedfordshireen
dc.contributor.departmentBauhaus-Universität Weimaren
dc.identifier.journalDatenbank-Spektrum : Zeitschrift fur Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft fur Informatik e.Ven
dc.identifier.pmcidPMC5750836
dc.date.updated2019-01-28T11:33:58Z
dc.description.noteoa article
html.description.abstractCyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.


Files in this item

Thumbnail
Name:
Frommholz2016_Article_OnTextua ...
Size:
478.4Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

http://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/