Clustering of Dark Patterns in the User Interfaces of Websites and Online Trading Portals (E-Commerce)

Dmitry Nazarov and Yerkebulan Baimukhambetov
Additional contact information
Dmitry Nazarov: Department of Business Informatics, Ural State University of Economics, 620144 Ekaterinburg, Russia
Yerkebulan Baimukhambetov: Head of the Institutional Effectiveness, Abai University, Almaty 050010, Kazakhstan

Mathematics, 2022, vol. 10, issue 18, 1-12

Abstract: dark patterns in the interfaces of users using sites and portals of online trading affect their behavior by companies that own digital resources. The authors propose to implement the detection of dark patterns on sites in user interfaces using cluster analysis algorithms using two methods for clustering many dark patterns in application interfaces: hierarchical and k-means. The complexity of the implementation lies in the lack of datasets that formalize dark patterns in user interfaces. The authors conducted a study and identified signs of dark patterns based on the use of Nelsen’s antisymmetric principles. The article proposes a technique for assessing dark patterns using linguistic variables and their further interval numerical assessment for implementing cluster data analysis. The last part of the article contains an analysis of two clustering algorithms and an analysis of the methods and procedures for applying them to clustering data according to previously selected features in the RStudio environment. We also gave a characteristic for each resulting cluster.

Keywords: dark pattern; classification; clustering algorithms; user interface (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/18/3219/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/18/3219/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:18:p:3219-:d:907634

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().