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On Textual Analysis and Machine Learning for Cyberstalking Detection.

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Summary
This summary is machine-generated.

Cybersecurity threats like cyberstalking are rising. This study explores text analytics and machine learning for detecting online harassment and gathering digital evidence, focusing on author identification.

Keywords:
Author identificationCyber harassmentCyber securityCyberstalkingMachine learningText analytics

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Area of Science:

  • Computer Science
  • Digital Forensics
  • Cybersecurity

Background:

  • Cybersecurity is a significant concern for individuals and organizations.
  • Cyberstalking and harassment represent a growing societal issue.
  • Victims often need to collect digital evidence for cyberstalking and harassment cases.

Purpose of the Study:

  • To provide an overview of technological solutions for cyberstalking and harassment detection.
  • To discuss the application of text analytics and machine learning in addressing these challenges.
  • To present a framework for detecting text-based cyberstalking and its associated forensic techniques.

Main Methods:

  • Text analytics and machine learning techniques are explored.
  • Core techniques discussed include author identification, text classification, and personalization.
  • The study references the PAN network and evaluation initiative for digital text forensics.

Main Results:

  • A framework for detecting text-based cyberstalking is presented.
  • The roles and challenges of key techniques like author identification are discussed.
  • The importance of digital text forensics, particularly author identification, is highlighted.

Conclusions:

  • Text analytics and machine learning offer viable solutions for cyberstalking and harassment detection.
  • Author identification is a crucial technique in digital text forensics for cybercrime investigations.
  • Further research and initiatives like PAN are essential for advancing cyber forensic capabilities.