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

EAGER：使用大规模网络数据进行在线注意力模型和阅读障碍识别

• 批准号: 1840751
• 负责人: Mor Naaman
• 金额: $ 29.88万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1840751&HistoricalAwards=false
• 关键词: EAGER; Using; Large; scale; Web

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.

媒体网站现在从数以百万计的读者那里获得了复杂的参与指标。这些测量，如页面内滚动和视口位置，可以帮助我们理解用户注意力的模式，而不仅仅是像在页面上花费的时间这样简单的测量。这个探索性项目将通过展示这些数据（主要是在在线新闻文章的背景下）如何更好地理解在线用户行为，并帮助大规模捕获（和推理）用户注意力，来改变这些未充分利用的注意力数据的使用。然而，这样的数据非常嘈杂，很难分析。研究人员将探索开发新的数据分析技术，以推断网络上语言、文本和注意力之间的联系。利用这些数据和新颖的分析技术，该项目还将探索如何识别有阅读困难的用户，并潜在地支持他们完成在线阅读任务。该项目的潜在成果是算法和方法，新闻内容提供商可以根据不同类型的读者互动和在线阅读新闻文章的方式，使用这些算法和方法来更好地理解不同类型的读者。更重要的是，潜在的结果还包括这些出版商如何发现和支持有阅读困难的用户。这种进步有可能改善7%至15%的阅读障碍人群的在线体验。该项目直接解决了使这种新型大规模数据在不同环境中有用和可用的多重挑战，并可能在大规模信息管理、机器学习和人机交互方面产生许多关键的智力贡献。第一个重大挑战是如何对原始的大规模数据进行建模和处理，以产生鲁棒的注意力信号。数据非常嘈杂，很难分析和理解，不同格式、不同内容类型的新闻文章被不同的用户群体使用。其次，该项目将通过使用新颖的深度学习技术来大规模地理解语言和注意力之间的相互作用，从而做出独特的贡献。这种建模可以扩展当前的自然语言处理（NLP）技术，以改进新闻文章上下文中的语言和叙事分析方法，并更广泛地用于阅读任务。最后，将需要实地研究和大规模数据分析相结合，以了解可能有阅读困难的用户的注意模式。实地研究将收集已知有阅读障碍的用户的数据，以便开发一种技术，可以帮助从大型交互数据集中的注意力数据中估计用户的阅读困难。因此，该项目将为结合NLP和人机交互的工作提供信息，以建议支持此类用户在线阅读任务的可能路径。为了支持进一步的研究和可重复性，本项目中使用的结果数据、软件和模型尽可能提供给其他研究人员。项目成果将通过研究论文和讲座向学术界和工业界的听众以及通过项目网站进行传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Understanding Local News Social Coverage and Engagement at Scale during the COVID-19 Pandemic

了解 COVID-19 大流行期间当地新闻的大规模社会报道和参与度

• DOI: 10.1609/icwsm.v16i1.19315
• 发表时间: 2022
• 期刊: Proceedings of the International AAAI Conference on Web and Social Media
• 作者: Le Quéré, Marianne Aubin;Chiang, Ting-Wei;Naaman, Mor
• 通讯作者: Naaman, Mor

Measuring and Understanding Online Reading Behaviors of People with Dyslexia

测量和理解阅读障碍者的在线阅读行为

• 发表时间: 2020
• 期刊: In submission
• 作者: Grusky, Max;Taft, Jessie;Naaman, Mor;Azenkot, Shiri
• 通讯作者: Azenkot, Shiri

Information Needs of Essential Workers During the COVID-19 Pandemic

COVID-19 大流行期间基本工作人员的信息需求

• 发表时间: 2022
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Aubin Le Quéré, Marianne;Chiang, Ting-Wei;Levy, Karen;Naaman, Mor
• 通讯作者: Naaman, Mor

Understanding Reader Backtracking Behavior in Online News Articles

了解在线新闻文章中的读者回溯行为

• DOI: 10.1145/3308558.3313571
• 发表时间: 2019
• 期刊: The World Wide Web Conference 2019
• 作者: Smadja, Uzi;Grusky, Max;Artzi, Yoav;Naaman, Mor
• 通讯作者: Naaman, Mor