EAGER: Using Large-scale Web Data for Online Attention Models and Identification of Reading Disabilities

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Media websites now capture intricate measures of engagement from millions of readers. These measures, such as in-page scrolling and viewport position, can help us understand patterns of user attention beyond simple measures like time spent on page. This exploratory project will transform the use of this under-utilized attention data by showing how such data, mostly in the context of online news articles, can lead to better understanding of user behavior online, and help capture (and reason about) user attention at scale. However, such data is extremely noisy and challenging to analyze. The researchers will explore developing new techniques for data analysis to reason about the connection between language, text, and attention on the Web. Using these data, and novel analysis techniques, the project will also explore how to identify users with reading difficulties, and potentially support them in online reading tasks. The potential outcomes of this project are algorithms and methods that can be used by news content providers to develop a refined understanding of different types of readers, according to how they interact and read news articles online. More importantly, potential outcomes also include advancements in how such publishers can detect, and support, users with reading difficulties. Such advancement has the potential to lead to improvements in online experiences for the estimated 7% to 15% of the population who suffer from reading disabilities. This project directly addresses multiple challenges in making this new type of large-scale data useful and usable in different settings, and is likely to result in a number of key intellectual contributions in large-scale information management, machine learning, and human computer interaction. The first significant challenge is in modeling and processing the raw large-scale data to result in a robust attention signals. The data is extremely noisy and challenging to analyze and understand, with news articles of different formats, different content types, used by different groups of users. Second, the project will make a unique contribution by using novel deep learning techniques to understand the interaction between language and attention, at scale. Such modeling can extend current Natural Language Processing (NLP) techniques to improve methods for the analysis of language and of narrative in the context of news articles, and more broadly for reading tasks. Finally, a combination of field studies and large-scale data analysis will be required to understand the attention patterns of users who may have difficulties in reading. A field study will collect data from users who are known to have reading disabilities, in order to develop a technique that can help estimate a user's reading difficulty from the attention data in a large interaction dataset. This project will thus inform work that combines NLP and Human-Computer Interaction to suggest possible paths for supporting such users in online reading tasks. In order to support further research and reproducibility, the resulting data, software, and models used in this project available to other researchers, as possible. The project results will be disseminated via research papers and talks to be academic and industry audiences, and through the project website.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.