A single digital newsletter usually contains many messages (regions). Users'
reading time spent on, and read level (skip/skim/read-in-detail) of each
message is important for platforms to understand their users' interests,
personalize their contents, and make recommendations. Based on accurate but
expensive-to-collect eyetracker-recorded data, we built models that predict
per-region reading time based on easy-to-collect Javascript browser tracking
data.
With eye-tracking, we collected 200k ground-truth datapoints on participants
reading news on browsers. Then we trained machine learning and deep learning
models to predict message-level reading time based on user interactions like
mouse position, scrolling, and clicking. We reached 27\% percentage error in
reading time estimation with a two-tower neural network based on user
interactions only, against the eye-tracking ground truth data, while the
heuristic baselines have around 46\% percentage error. We also discovered the
benefits of replacing per-session models with per-timestamp models, and adding
user pattern features. We concluded with suggestions on developing
message-level reading estimation techniques based on available data.

利用机器学习和深度学习模型，可以根据易于收集的 Javascript 浏览器跟踪数据，准确预测用户阅读数字通讯中每个信息（区域）的阅读时间，可用于优化个性化内容推荐和提高用户体验。

充分利用眼动追踪技术：基于用户交互的阅读区域估计数据集和模型

Getting the Most from Eye-Tracking: User-Interaction Based Reading  Region Estimation Dataset and Models

Numerous analyses of reading time (RT) data have been implemented -- all in
an effort to better understand the cognitive processes driving reading
comprehension. However, data measured on words at the end of a sentence -- or
even at the end of a clause -- is often omitted due to the confounding factors
introduced by so-called "wrap-up effects," which manifests as a skewed
distribution of RTs for these words. Consequently, the understanding of the
cognitive processes that might be involved in these wrap-up effects is limited.
In this work, we attempt to learn more about these processes by examining the
relationship between wrap-up effects and information-theoretic quantities, such
as word and context surprisals. We find that the distribution of information in
prior contexts is often predictive of sentence- and clause-final RTs (while not
of sentence-medial RTs). This lends support to several prior hypotheses about
the processes involved in wrap-up effects.

研究文章探讨了阅读时间数据的分析，重点是学习 “wrap-up effects” 影响下话语末尾单词的认知过程，并发现先前背景信息的分布往往预测了句子和从句结尾的阅读时间。

通过信息论视角分析总结效应

Analyzing Wrap-Up Effects through an Information-Theoretic Lens

The uniform information density (UID) hypothesis posits a preference among
language users for utterances structured such that information is distributed
uniformly across a signal. While its implications on language production have
been well explored, the hypothesis potentially makes predictions about language
comprehension and linguistic acceptability as well. Further, it is unclear how
uniformity in a linguistic signal -- or lack thereof -- should be measured, and
over which linguistic unit, e.g., the sentence or language level, this
uniformity should hold. Here we investigate these facets of the UID hypothesis
using reading time and acceptability data. While our reading time results are
generally consistent with previous work, they are also consistent with a weakly
super-linear effect of surprisal, which would be compatible with UID's
predictions. For acceptability judgments, we find clearer evidence that
non-uniformity in information density is predictive of lower acceptability. We
then explore multiple operationalizations of UID, motivated by different
interpretations of the original hypothesis, and analyze the scope over which
the pressure towards uniformity is exerted. The explanatory power of a subset
of the proposed operationalizations suggests that the strongest trend may be a
regression towards a mean surprisal across the language, rather than the
phrase, sentence, or document -- a finding that supports a typical
interpretation of UID, namely that it is the byproduct of language users
maximizing the use of a (hypothetical) communication channel.

本文研究了统一信息密度假说对于语言理解和语言可接受性的影响，使用阅读时间和可接受性数据，发现信息密度的不均匀性会降低可接受性，而统一信息密度假说可以部分解释语言压缩的趋势。