Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users

Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web br...

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Main Authors: Satwik Ram Kodandaram, Mohan Sunkara, Sampath Jayarathna, Vikas Ashok
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Journal of Imaging
Subjects:
Online Access:https://www.mdpi.com/2313-433X/9/11/239
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author Satwik Ram Kodandaram
Mohan Sunkara
Sampath Jayarathna
Vikas Ashok
author_facet Satwik Ram Kodandaram
Mohan Sunkara
Sampath Jayarathna
Vikas Ashok
author_sort Satwik Ram Kodandaram
collection DOAJ
description Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users’ experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via a blanket filtering operation, many websites are increasingly denying access if an ad blocker is active. Moreover, ad blockers often do not filter out <i>internal</i> ads injected by the websites themselves. Therefore, we devised an algorithm to automatically identify contextually deceptive ads on a web page. Specifically, we built a detection model that leverages a multi-modal combination of handcrafted and automatically extracted features to determine if a particular ad is contextually deceptive. Evaluations of the model on a representative test dataset and ‘in-the-wild’ random websites yielded F1 scores of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.86</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.88</mn></mrow></semantics></math></inline-formula>, respectively.
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spelling doaj.art-742bc8855fea459dbc9520cf3ee831ff2023-11-24T14:50:03ZengMDPI AGJournal of Imaging2313-433X2023-11-0191123910.3390/jimaging9110239Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader UsersSatwik Ram Kodandaram0Mohan Sunkara1Sampath Jayarathna2Vikas Ashok3Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USADepartment of Computer Science, Old Dominion University, Norfolk, VA 23529, USADepartment of Computer Science, Old Dominion University, Norfolk, VA 23529, USADepartment of Computer Science, Old Dominion University, Norfolk, VA 23529, USAAdvertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users’ experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via a blanket filtering operation, many websites are increasingly denying access if an ad blocker is active. Moreover, ad blockers often do not filter out <i>internal</i> ads injected by the websites themselves. Therefore, we devised an algorithm to automatically identify contextually deceptive ads on a web page. Specifically, we built a detection model that leverages a multi-modal combination of handcrafted and automatically extracted features to determine if a particular ad is contextually deceptive. Evaluations of the model on a representative test dataset and ‘in-the-wild’ random websites yielded F1 scores of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.86</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.88</mn></mrow></semantics></math></inline-formula>, respectively.https://www.mdpi.com/2313-433X/9/11/239extraneous contentdeceptive advertsad blockersdark patternsblind usersscreen readers
spellingShingle Satwik Ram Kodandaram
Mohan Sunkara
Sampath Jayarathna
Vikas Ashok
Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users
Journal of Imaging
extraneous content
deceptive adverts
ad blockers
dark patterns
blind users
screen readers
title Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users
title_full Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users
title_fullStr Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users
title_full_unstemmed Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users
title_short Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users
title_sort detecting deceptive dark pattern web advertisements for blind screen reader users
topic extraneous content
deceptive adverts
ad blockers
dark patterns
blind users
screen readers
url https://www.mdpi.com/2313-433X/9/11/239
work_keys_str_mv AT satwikramkodandaram detectingdeceptivedarkpatternwebadvertisementsforblindscreenreaderusers
AT mohansunkara detectingdeceptivedarkpatternwebadvertisementsforblindscreenreaderusers
AT sampathjayarathna detectingdeceptivedarkpatternwebadvertisementsforblindscreenreaderusers
AT vikasashok detectingdeceptivedarkpatternwebadvertisementsforblindscreenreaderusers