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Datenbank Spektrum. 2016;16(2):127-135. doi: 10.1007/s13222-016-0221-x. Epub 2016 Jun 1.

On Textual Analysis and Machine Learning for Cyberstalking Detection.

Author information

1
The National Centre for Cyberstalking Research, Institute for Research in Applicable Computing, University of Bedfordshire, Luton, UK.
2
Web Technology and Information Systems, Bauhaus-Universität Weimar, Weimar, Germany.

Abstract

Cyber 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.

KEYWORDS:

Author identification; Cyber harassment; Cyber security; Cyberstalking; Machine learning; Text analytics

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