WordPress Accessibility Scan Results  ## Metadata - Author: [[Equalify]] - Full Title: WordPress Accessibility Scan Results - Category: #articles - Document Note: --- tags: [WordPress, AccessibilityScan, LLM, DataVisualization, WebAccessibility] - WordPress - AccessibilityScan - DataVisualization Author: Blake Source: WordPress Accessibility Scan Results Reference: [YouTube Video](https://www.youtube.com/watch?v=16cX9C33LvE) date created: 2024-09-15 11:00 --- # Title: WordPress Accessibility Scan Results ## Table of Contents: - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [Accessibility Scan Overview](#accessibility-scan-overview) - [Methodology Used for Scanning](#methodology-used-for-scanning) - [Issues Identified](#issues-identified) - [Data Analysis and Next Steps](#data-analysis-and-next-steps) - [🗒️ Notes](#-notes) - [📖 References](#-references) - [🗣️ Paraphrase](#-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical Validity](#-logical-validity) - [🔍 SEO Evaluation](#-seo-evaluation) - [🎯 Conclusion](#-conclusion) ## 📝 Summary: In this project, Blake and his team conducted a large-scale accessibility scan on WordPress sites using the Equalify API. The scan focused on identifying accessibility issues across 445 properties with over 115,000 URLs. The team used tools like Axe and fine-tuned scans to improve accuracy. The results show over 1,100 different types of accessibility issues across 1.6 million nodes. The next step is to use data visualization and an LLM system to categorize and address these issues, which will eventually be open-sourced for community input. ## 📖 Main Content: ### Accessibility Scan Overview: - The team scanned all known WordPress sites to identify accessibility issues. - Equalify API was used to discover sites, collect data, and scan for accessibility concerns. ### Methodology Used for Scanning: - The team used a combination of crawls, sitemap scans, and single-page scans. - Sitemap scans proved to be the most effective, generating a dataset of 115,000 URLs. ### Issues Identified: - A total of 1,135 distinct types of accessibility issues were discovered across 1.6 million HTML nodes. - Accessibility issues were further categorized into 71 different tags for easier management. ### Data Analysis and Next Steps: - The team plans to visualize the issues using data visualization tools. - They are also integrating an LLM system to analyze and categorize patterns within the accessibility data. - Blake plans to share the data on GitHub for open collaboration with the developer community. ## 🗒️ Notes: - 445 WordPress properties were scanned, with 115,000 URLs analyzed. - The main tool used for scanning was Axe, fine-tuned to reduce false positives. - Next steps include data visualization and using an LLM to triage the accessibility issues for better resolution. ## 📖 References: - Blake, WordPress Accessibility Scan Results, [YouTube Video](https://www.youtube.com/watch?v=16cX9C33LvE) ## 🗣️ Paraphrase: 1. **Methodology**: Blake's team used a mixture of different scanning methods, with the sitemap scan emerging as the most accurate. This approach generated a large volume of data, which is now being processed. 2. **Next Steps**: Blake emphasized the importance of data visualization and leveraging an LLM system to understand patterns in accessibility issues. This will help triage the issues more efficiently and inform actionable steps. ## 📰 Newsletter: **WordPress Accessibility at Scale: Data Insights and Tools** Blake's team recently scanned 445 WordPress properties, uncovering over 1,100 types of accessibility issues across more than 115,000 URLs. Using advanced tools like Equalify and Axe, the team gathered data that will be open-sourced for community collaboration. Next up, the integration of an LLM system to analyze and visualize accessibility issues will help address these problems faster. Want to improve your site's accessibility? Stay tuned for actionable insights from this groundbreaking project. ## ✅ Action Items: 1. Share the scan results on GitHub for community collaboration. 2. Finalize and implement the LLM system to better analyze and categorize accessibility issues. 3. Develop data visualizations to assist in understanding and addressing the problems. ## 🧪 Hypotheses: - Integrating an LLM into accessibility issue analysis will increase the accuracy of issue categorization by 30%. - Community collaboration on GitHub will help resolve at least 20% of identified issues within the first 6 months. ## 📊 Methodology: - Use the Equalify API for site discovery and the Axe tool for accessibility scanning. - Leverage LLMs to assist in categorizing and triaging accessibility issues based on data patterns. ## ❓ Questions: - What challenges could arise when scaling this accessibility scan to additional websites? - How effective is the LLM system in prioritizing and categorizing accessibility issues? ## 🔮 Future Research: - Explore the effectiveness of LLMs in improving accessibility issue identification over traditional tools. - Study the impact of open-source collaboration on addressing accessibility concerns across the WordPress ecosystem. ## 💡 Implications: Successfully integrating LLMs and open collaboration could set new standards for how large-scale accessibility scans are performed and addressed in the WordPress community. ## 🧠 Learning Styles Assessment: 1. **Visual Learners**: The team plans to use data visualization to make complex patterns in accessibility issues easier to understand. 2. **Logical Learners**: Detailed data and structured tagging of issues provide a logical progression for problem-solving. ## 📊 Accessibility Assessment: - **Screen Reader Compatibility**: The video explanation could benefit from added captions and transcripts. - **Text Resizing**: Ensure data visualizations are accessible and scalable for users with visual impairments. ## 🌍 DEIB Assessment: - The project could highlight accessibility challenges faced by diverse users, ensuring the solution is inclusive for all demographics. ## ✅ Logical Validity: - The project follows sound logical reasoning by addressing known accessibility challenges and using data to triage problems. - However, assumptions about LLM performance could be tested further. ## 🔍 SEO Evaluation: - **Keywords**: "WordPress accessibility scan" and "LLM" are well-placed in the title and description. - **Meta Description**: Include a brief sentence about the use of LLMs for accessibility triage to enhance SEO. ## 🎯 Conclusion: Blake’s accessibility scan offers a comprehensive look at common accessibility issues across WordPress sites. With plans to open-source the data and leverage LLMs for triage, this initiative holds significant potential for improving accessibility standards within the WordPress community. Short Summary for [WordPress Accessibility Scan Results](https://www.youtube.com/watch?v=16cX9C33LvE) by [Merlin](https://merlin.foyer.work/) Title: WordPress Accessibility Scan Results: Finding and Fixing Accessibility Issues for WordPress Websites [00:02](https://www.youtube.com/watch?v=16cX9C33LvE&t=2) WordPress Accessibility Scan Results [00:39](https://www.youtube.com/watch?v=16cX9C33LvE&t=39) Utilized Qualify API for efficient accessibility scan results [01:13](https://www.youtube.com/watch?v=16cX9C33LvE&t=73) Tweaked scan reduced false positives [01:53](https://www.youtube.com/watch?v=16cX9C33LvE&t=113) Sitemap scan resulted in most accurate pages. [02:20](https://www.youtube.com/watch?v=16cX9C33LvE&t=140) Scan all pages for accessibility issues [02:58](https://www.youtube.com/watch?v=16cX9C33LvE&t=178) Identified 1,135 accessibility issues, segmented into 71 tags. [03:38](https://www.youtube.com/watch?v=16cX9C33LvE&t=218) LLM system analyzes issues and patterns. [04:06](https://www.youtube.com/watch?v=16cX9C33LvE&t=246) Data visualization for triaging accessibility issues - Document Tags: [[accessibility]] [[wordpress]] - Summary: Equalify conducted a WordPress accessibility scan on numerous websites, finding various accessibility issues. They used a specialized scan tool called axe to identify and categorize over 1,100 accessibility problems across 115,000 URLs. They are now working on visualizing the data, categorizing the issues, and developing an analysis system to address and prioritize the accessibility improvements. - URL: https://www.youtube.com/watch?v=16cX9C33LvE hey everybody uh Blake here with my little hiccupping baby Willa um today I'm going to show you the results of our giant WordPress scan um so we scanned all of the uh known WordPress sites for accessibility errors um and we have some pretty cool results so just to kind of like show what we did the process um we went through and collected every single WordPress site that we possibly could uh this is through a domain Discovery feature that he qualify has um and then since there were so many sites um we you know wanted to do it in a very efficient way so uh we have a a qualify API and basically the way this works is we sent the URLs uh of the sites or site Maps um to equalify and then equalify gave us um a bunch of data and the data is um uh in the qualified schema format um but the data will show all the different accessibility issues um using the scans that we have active for WordPress uh we have axe active that that was our primary scan that we use we did kind of tune it a little bit to make sure we weren't getting as many false positives as just aore out of the box um but in the end here's what we did so in the end we had 445 properties these are just different sites um within a.org system and then um we had from these properties we were able to get um here it is uh 115,000 URLs um so we trimmed some of the URLs here um we did try a uh a few different types of scans so we initially did a um a crawl and then we did a sitemap scan and then we just did single page scans um and then the the site map scan was the the best I think it it it got not only the most pages but the most accurate Pages um so that's what we kind of kept with so the vast majority of these 115,000 pages are coming from the sitemap scan I think actually all of the 115,000 pages are coming from the sit map scan and then so with this these page URLs then a qualify works at Magic so we then go and scan all the pages for accessibility issues and we're looking at particular nodes on the pages so what I have on screen right now is just our database of all the nodes and nodes are basically HTML Snippets um that you would find that populate the Dom and so we have uh 1,638 453 nodes um and these nodes are where we can find accessibility issues um and so there are a total of 1,135 different types of accessibility issues that we we uh came across um and these are further segmented into uh tags so there are um let's see here there are uh 7 71 tags for these issues now our next process is to both visualize these issues kind of understand what we have and where it is and then also um better categorize the um accessibility issues so that we can address them and we're not flooding um uh with requests to update them um we're building currently an llm system that's going to analyze all of these different issues it's going to find where there's patterns and things like that um and the llm system again is not an end all Beall it's it's basically like a co-pilot system um in addition to that we really want to see what people do with this data visualization so I'm going to post on GitHub um all this data and you know I would really love to hear what people do with it and and what people uh see with it um meanwhile you know again this this month I'm going to be working on data visualization and then we're going to be feeding our our new llm into it to see if we can get some really good understanding of how to triage these issues what they are and then uh get some marching steps move forward so again uh check out the GitHub repo that I'm going to post and uh yeah thank you for watching, WordPress Accessibility Scan Results, Equalify, 01hxckry7k31c9ne5rqhq091yy.