Algorithmic thinking in the public interest: navigating technical, legal, and ethical hurdles to web scraping in the social sciences

Alex Luscombe (), Kevin Dick and Kevin Walby
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Alex Luscombe: University of Toronto
Kevin Dick: Carleton University
Kevin Walby: University of Winnipeg

Quality & Quantity: International Journal of Methodology, 2022, vol. 56, issue 3, No 6, 1023-1044

Abstract: Abstract Web scraping, defined as the automated extraction of information online, is an increasingly important means of producing data in the social sciences. We contribute to emerging social science literature on computational methods by elaborating on web scraping as a means of automated access to information. We begin by situating the practice of web scraping in context, providing an overview of how it works and how it compares to other methods in the social sciences. Next, we assess the benefits and challenges of scraping as a technique of information production. In terms of benefits, we highlight how scraping can help researchers answer new questions, supersede limits in official data, overcome access hurdles, and reinvigorate the values of sharing, openness, and trust in the social sciences. In terms of challenges, we discuss three: technical, legal, and ethical. By adopting “algorithmic thinking in the public interest” as a way of navigating these hurdles, researchers can improve the state of access to information on the Internet while also contributing to scholarly discussions about the legality and ethics of web scraping. Example software accompanying this article are available within the supplementary materials.

Keywords: Web scraping; Digital methods; Law; Ethics; Algorithmic thinking; Access to information; Social science research (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11135-021-01164-0

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