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.

现在，媒体网站捕获了数百万读者的复杂参与度量。这些措施（例如页面滚动和视口位置）可以帮助我们了解用户注意的模式，除了简单的措施（例如在页面上花费的时间）。这个探索性项目将通过显示该数据（主要是在线新闻文章的背景下）如何使对用户行为在线更好地理解（以及帮助捕获（以及理由）对用户的关注的情况下，该数据将如何改变这种未充分利用的注意力数据的使用。但是，这样的数据非常嘈杂且具有挑战性。研究人员将探索开发用于数据分析的新技术，以推理网络上语言，文本和关注之间的联系。使用这些数据和新颖的分析技术，该项目还将探索如何在阅读困难中识别用户，并有可能在在线阅读任务中支持他们。该项目的潜在结果是新闻内容提供商可以使用的算法和方法，以根据他们在线互动和阅读新闻文章的方式来开发对不同类型读者的精致理解。更重要的是，潜在的结果还包括在此类发布者如何检测和支持阅读困难的用户方面的进步。这样的进步有可能导致在在线体验中的改善，估计有7％至15％的阅读障碍人群。该项目直接解决了使这种新型的大规模数据有用并在不同设置中使用的多个挑战，并且很可能在大规模信息管理，机器学习和人类计算机交互中产生许多关键的知识贡献。第一个重大挑战是建模和处理原始的大规模数据，以产生强大的注意力信号。这些数据非常嘈杂，可以通过不同的形式，不同的内容类型的新闻文章（不同的用户组使用）进行分析和理解。其次，该项目将通过使用新颖的深度学习技术来理解语言与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，以改善新闻文章中语言和叙事分析的方法，更广泛地用于阅读任务。最后，将需要进行现场研究和大规模数据分析的结合，以了解可能难以阅读的用户的注意力模式。一项现场研究将从已知有阅读障碍的用户那里收集数据，以开发一种可以帮助估算大型交互数据集中注意数据的用户阅读难度的技术。因此，该项目将为NLP和人类计算机互动结合的工作提供信息，以提出可能支持此类用户在线阅读任务中的途径。为了支持进一步的研究和可重复性，最终的数据，软件和模型以及其他研究人员可用的模型。该项目的结果将通过研究论文进行传播，并通过学术和行业受众进行谈判，并通过项目网站进行演讲。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。

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

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

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