Understanding the Impact of Dark Pattern Detection on Online Users

Dark Patterns are a variety of different software designs that are used to manipulate and mislead the users of an application or service. These patterns range from making it harder to end a subscription service, adding additional charges to a purchase, or having the user give out data or personal information. With how widespread and varied dark patterns are, it led to us creating a way to detect and warn users of different dark patterns. In this study, we created Dark Pattern Detector, a Chrome extension that would help users detect and understand three different dark patterns: Hidden Costs, Disguised Ads, and Sneak into Basket. This extension was made to detect each of these patterns on any web page while not requiring any information from the user or their data. Study participants installed the extension and completed a series of tasks given to them that would occur on different websites containing the previous dark patterns. After completing the tasks, the users were surveyed to give feedback on what they thought of the extension and what suggestions for change they had. In the study, we had 40 participants and we found that 50% of the users were completely unfamiliar with dark patterns and that 77.5% have used extensions before. For the five tasks, each one had a majority of the participants successfully complete them. Finally, when asked about what they thought, the majority of the participants gave positive feedback claiming that they found the extension useful, interesting, and a good idea. Many participants also gave useful feedback about what changes or additions they would like to see. With our results, we can help users have a better understanding of dark patterns and have created a baseline for any future research done on dark pattern knowledge and detection.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Wood, Ryan Matthew

Chair dc:contributor.committeechair

• Brown, Dwayne Christian

Committee members dc:contributor.committeemember

• Dunlap, Daniel R.
• McCrickard, Donald Scott

• Dark Patterns
• Deceptive Design
• User Study

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37952

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115787