Deceptive Patterns
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Analysis of Dark Pattern-related Tweets from 2010

Author
Jiaying Feng, Fan Mo, Yukiharu Yada, Tsuneo Matsumoto, Naotake Fukushima, Fukuyo Kido, H. Yamana
Date
3 Mar 2023
Publisher
2023 IEEE 8th International Conference on Big Data Analytics (ICBDA)
Focus
Historical & Cultural
Category
Academic Scholar

This study reveals the users’ responses toward dark patterns by analyzing 12 years of tweets, which may help policymakers and regulators to promote the more secure internet use.

Dark patterns are defined as user interfaces that make users behave unintendedly, such as buying something or subscribing to some services. The use of dark patterns is considered an infringement of users’ rights and privacy. In this study, we reveal the users’ responses toward dark patterns by analyzing 12 years of tweets. Our findings include 1) users in countries in which dark patterns-related regulations have been implemented have a higher level of discussions about dark patterns; 2) tweets about dark patterns shifted from around 2017 from sharing their diversity to acting to resist them; 3) the commonly discussed dark pattern types of tweets are sneaking, obstruction, and interface interference, which are widely used in e-commerce sites.