On Textual Analysis and Machine Learning for Cyberstalking Detection


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.

Publication DOI: https://doi.org/10.1007/s13222-016-0221-x
Divisions: College of Business and Social Sciences > Aston Business School > Cyber Security Innovation (CSI) Research Centre
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Uncontrolled Keywords: Cyber security,Cyberstalking,Cyber harassment,Text analysis,Machine learning,Author identification
Publication ISSN: 1618-2162
Last Modified: 17 Apr 2024 07:25
Date Deposited: 24 Jan 2023 16:38
Full Text Link:
Related URLs: https://link.sp ... 3222-016-0221-x (Publisher URL)
PURE Output Type: Article
Published Date: 2016-07
Published Online Date: 2016-06-01
Accepted Date: 2016-04-21
Authors: Frommholz, Ingo
Al-Khateeb, Haider (ORCID Profile 0000-0001-8944-123X)
Potthast, Martin
Ghasem, Zinnar
Shukla, Mitul
Short, Emma



Version: Published Version

License: Creative Commons Attribution

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