JOURNAL ARTICLE
Establishing a framework for the ethical and legal use of web scrapers by cybercrime and cybersecurity researchers: learnings from a systematic review of Australian research.
Published In: International Journal of Law & Information Technology, 2023, v. 31, n. 3. P. 186 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Logos, Katie; Brewer, Russell; Langos, Colette; Westlake, Bryce 3 of 3
Abstract
This article examines the ethical and legal frameworks governing the use of automated data collection technologies (ADCT)—including web crawlers and web scrapers—in Australian cybersecurity research. Through a systematic review of 30 Australian-affiliated studies deploying ADCT across various online domains such as dark web marketplaces, social media, and e-commerce sites, the article identifies key risks including privacy violations, potential breaches of website Terms of Use, copyright infringement, and possession of illegal content. It analyzes these challenges within the context of Australian legislation, notably the Data and Availability and Transparency Act 2022, the Privacy Act 1988, the Copyright Act 1968, and relevant criminal codes, alongside ethical guidelines from the National Statement for Ethical Conduct in Human Research. The article concludes that ADCT use can be both ethical and legal if mitigating measures—such as data de-identification, limiting data collection scope, securing data storage, and obtaining ethical approvals—are implemented, and it offers practical directions to guide researchers navigating this complex and evolving landscape.
Additional Information
- Source:International Journal of Law & Information Technology. 2023/09, Vol. 31, Issue 3, p186
- Document Type:Article
- Subject Area:Communication and Mass Media
- Publication Date:2023
- ISSN:09670769
- DOI:10.1093/ijlit/eaad023
- Accession Number:173894659
- Copyright Statement:Copyright of International Journal of Law & Information Technology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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