Millions of people turn to Google Search each day for information on things
as diverse as new cars or flu symptoms. The terms that they enter contain
valuable information on their daily intent and activities, but the information
in these search terms has been difficult to fully leverage. User-defined
categorical filters have been the most common way to shrink the dimensionality
of search data to a tractable size for analysis and modeling. In this paper we
present a new approach to reducing the dimensionality of search data while
retaining much of the information in the individual terms without user-defined
rules. Our contributions are two-fold: 1) we introduce SLaM Compression, a way
to quantify search terms using pre-trained language models and create a
representation of search data that has low dimensionality, is memory efficient,
and effectively acts as a summary of search, and 2) we present CoSMo, a
Constrained Search Model for estimating real world events using only search
data. We demonstrate the efficacy of our contributions by estimating with high
accuracy U.S. automobile sales and U.S. flu rates using only Google Search
data.

通过使用预训练语言模型量化搜索词并创建低维度、内存高效且有效地充当搜索摘要的搜索数据表示方法，以及使用仅有的搜索数据估计真实世界事件的约束搜索模型（CoSMo），本文展示了使用仅有的 Google 搜索数据高准确度地估计美国汽车销售和流感率。

使用语言模型压缩搜索

Compressing Search with Language Models

It is widely known that Google Trends have become one of the most popular
free tools used by forecasters both in academics and in the private and public
sectors. There are many papers, from several different fields, concluding that
Google Trends improve forecasts' accuracy. However, what seems to be widely
unknown, is that each sample of Google search data is different from the other,
even if you set the same search term, data and location. This means that it is
possible to find arbitrary conclusions merely by chance. This paper aims to
show why and when it can become a problem and how to overcome this obstacle.

本文研究了 Google Trends 在提高预测准确性方面的应用已经面临的障碍，重点分析了不同的数据样本及其偶然性对准确性的影响。

使用 Google Trends 正确地预测商品需求

The Proper Use of Google Trends in Forecasting Models

The lack of comprehensive, high-quality health data in developing nations
creates a roadblock for combating the impacts of disease. One key challenge is
understanding the health information needs of people in these nations. Without
understanding people's everyday needs, concerns, and misconceptions, health
organizations and policymakers lack the ability to effectively target education
and programming efforts. In this paper, we propose a bottom-up approach that
uses search data from individuals to uncover and gain insight into health
information needs in Africa. We analyze Bing searches related to HIV/AIDS,
malaria, and tuberculosis from all 54 African nations. For each disease, we
automatically derive a set of common search themes or topics, revealing a
wide-spread interest in various types of information, including disease
symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in
natural cures, and other topics that may be hard to uncover through traditional
surveys. We expose the different patterns that emerge in health information
needs by demographic groups (age and sex) and country. We also uncover
discrepancies in the quality of content returned by search engines to users by
topic. Combined, our results suggest that search data can help illuminate
health information needs in Africa and inform discussions on health policy and
targeted education efforts both on- and offline.

使用搜索数据研究发展中国家健康信息需求，结果表明这种方法可以帮助制定和完善医疗政策和在线、离线教育计划，同时对于调查难以发现的相关问题，比如乳汁喂养，社会歧视等更有针对性。