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. / Master of Science / Dark patterns are designs on the internet that websites use to trick its users. They may be used to hide advertisements, make the user spend more time or money on their website or more. Our goal was to create a way to help protect anyone on the internet and their information.
For this study, we created a program called Dark Pattern Detector that would help the users see different dark patterns that appeared on websites. A study was conducted that had the participants use our program and give us feedback on what they thought of it as well as data on how well it worked. Out of the 40 participants, we found that half the users were unfamiliar with what dark patterns were. Once they completed the study, we saw that the majority of users were able to complete tasks while using our program and gave positive feedback.
Seeing the positive feedback and results from our study, we believe that we can help users not get tricked by these patterns and help forward future research on Dark Patterns.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115787 |
| Date | 17 July 2023 |
| Creators | Wood, Ryan Matthew |
| Contributors | Computer Science and Applications, Brown, Dwayne Christian, Dunlap, Daniel R., McCrickard, Donald Scott |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.0026 seconds