Detecting Deceptive Dark Patterns in E-commerce Platforms
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866929371562901504 |
|---|---|
| author | Ramteke, Arya Tembhurne, Sankalp Sonawane, Gunesh Bhimanpallewar, Ratnmala N. |
| author_facet | Ramteke, Arya Tembhurne, Sankalp Sonawane, Gunesh Bhimanpallewar, Ratnmala N. |
| contents | Dark patterns are deceptive user interfaces employed by e-commerce websites to manipulate user's behavior in a way that benefits the website, often unethically. This study investigates the detection of such dark patterns. Existing solutions include UIGuard, which uses computer vision and natural language processing, and approaches that categorize dark patterns based on detectability or utilize machine learning models trained on datasets. We propose combining web scraping techniques with fine-tuned BERT language models and generative capabilities to identify dark patterns, including outliers. The approach scrapes textual content, feeds it into the BERT model for detection, and leverages BERT's bidirectional analysis and generation abilities. The study builds upon research on automatically detecting and explaining dark patterns, aiming to raise awareness and protect consumers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01608 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Deceptive Dark Patterns in E-commerce Platforms Ramteke, Arya Tembhurne, Sankalp Sonawane, Gunesh Bhimanpallewar, Ratnmala N. Information Retrieval Artificial Intelligence Computation and Language Human-Computer Interaction Dark patterns are deceptive user interfaces employed by e-commerce websites to manipulate user's behavior in a way that benefits the website, often unethically. This study investigates the detection of such dark patterns. Existing solutions include UIGuard, which uses computer vision and natural language processing, and approaches that categorize dark patterns based on detectability or utilize machine learning models trained on datasets. We propose combining web scraping techniques with fine-tuned BERT language models and generative capabilities to identify dark patterns, including outliers. The approach scrapes textual content, feeds it into the BERT model for detection, and leverages BERT's bidirectional analysis and generation abilities. The study builds upon research on automatically detecting and explaining dark patterns, aiming to raise awareness and protect consumers. |
| title | Detecting Deceptive Dark Patterns in E-commerce Platforms |
| topic | Information Retrieval Artificial Intelligence Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.01608 |