Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics
Home Institution, City, State
Virginia Wesleyan University, Virginia Beach, Virginia
Visually impaired people who want to use a computer rely on screen readers to independently do this. This research focuses on beginning to build a chrome extension in order to help users more safely navigate the internet using a screen reader. to begin collecting the data, a screen reader was used to help determine items in the website that might take the user somewhere they did not mean to go since the link or image was not sufficiently able to be described by the screen reader. Next, those items were tagged with ’data-attribute=”deceptive”’. After, those data-attributes were extracted and tagged with values for various features in it, and a code at the end for if it was a deceptive item. Then six different machine learning models were created in order to predict whether an item on a website is deceptive. Overall, the best model for this data set was the Random Forest Classification from the Scikit-Learn Python Library. Overall, there is much more to be done to improve the accuracy and usability of the models, and then develop the chrome extension, but this is research created a point to begin from for future research.
Visually-impaired, Blind, Screen readers, Chrome extension, Deceptive Web content
Accessibility | Computer Sciences | Software Engineering
Dobrenen, Ash and Ashok, Vikas (Mentor), "Protecting Blind Screen-Reader Users From Deceptive Content" (2022). Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics. 4.
Final paper is included as an additional file.
© Copyright, Anna Dobrenen, 2022