Understanding the spread of images across the web helps us understand the
reuse of scientific visualizations and their relationship with the public. The
"Flatten the Curve" graphic was heavily used during the COVID-19 pandemic to
convey a complex concept in a simple form. It displays two curves comparing the
impact on case loads for medical facilities if the populace either adopts or
fails to adopt protective measures during a pandemic. We use five variants of
the "Flatten the Curve" image as a case study for viewing the spread of an
image online. To evaluate its spread, we leverage three information channels:
reverse image search engines, social media, and web archives. Reverse image
searches give us a current view into image reuse. Social media helps us
understand a variant's popularity over time. Web archives help us see when it
was preserved, highlighting a view of popularity for future researchers. Our
case study leverages document URLs can be used as a proxy for images when
studying the spread of images online.

本研究基于五个变体的 “压扁曲线” 图像，通过反向图像搜索引擎、社交媒体和 Web 档案等三个信息渠道，探究了图像在网络上的传播；文档 URL 还可作为在线图像传播研究的代理。

在线发现图片使用情况：以 “压平曲线” 为案例研究

Discovering Image Usage Online: A Case Study With "Flatten the Curve''

This document describes the aggregation and anonymization process applied to
the initial version of Google COVID-19 Community Mobility Reports (published at
this http URL on April 2, 2020), a publicly available
resource intended to help public health authorities understand what has changed
in response to work-from-home, shelter-in-place, and other recommended policies
aimed at flattening the curve of the COVID-19 pandemic. Our anonymization
process is designed to ensure that no personal data, including an individual's
location, movement, or contacts, can be derived from the resulting metrics.
The high-level description of the procedure is as follows: we first generate
a set of anonymized metrics from the data of Google users who opted in to
Location History. Then, we compute percentage changes of these metrics from a
baseline based on the historical part of the anonymized metrics. We then
discard a subset which does not meet our bar for statistical reliability, and
release the rest publicly in a format that compares the result to the private
baseline.

该文描述了应用于 Google COVID-19 社区流动性报告的聚合和匿名化过程，以帮助公共卫生当局理解在 COVID-19 大流行期间对工作、居家隔离和其他推荐政策的回应，其匿名化过程旨在确保不会从生成的指标中导出任何个人数据，包括个人位置、移动或联系方式。