Enriching the web by modeling reading difficulty
Kevyn Collins‐Thompson · 2013
The ability to read and understand a text would seem to be a basic aspect of interacting with a rich information source like the Web, yet little is currently known about the nature of the Web, its users, and how users interact with content when seen through the lens of reading difficulty. For example, a document isn't relevant to a person's information need - at least, not immediately - if they can't understand it, yet Web search engines have traditionally ignored the problem of finding or providing content at the right level of difficulty as an aspect of relevance. I'll give an overview of recent research that shows how computing and applying metadata based on text readability at Web scale opens up new and sometimes surprising possibilities for enriching our interactions with the Web: from personalizing Web search results, to predicting user and site expertise, to estimating searcher motivation. I'll also highlight future challenges and opportunities in improving text readability analysis, particularly in light of the rapidly growing interest in large-scale applications for online education.