searching covid‑19

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Telling the story of the first few months of the Covid-19 pandemic through the lens of Google search behavior. An interactive web-based visualization project.

In partnership with Google News Initiative and Axios, with art direction by Alberto Cairo.

I lead the small team that collected and analyzed raw search trend data, constructed narratives from our findings, prototyped different visualization approaches, and built the final website.


This project began with a simple question: Could trends in search behavior capture how individuals across the US (and rest of the world) were responding to the Covid-19 pandemic?

What types of questions were people seeking the answers to? How were those questions changing over time?


We used the Google Trends API to collect raw data on the top trending Covid-19 related search queries on each day between Jan and May 2020. We restricted this search to only include queries that began with the questions "How to ____" or "What is/are ____", and collected data from all 50 states and 5 additional English-speaking countries. This approach yielded over 26,000 Covid-19 related queries.

Then, each query was manually classified along a number of dimensions, including category (e.g. "Symptoms & Treatment"), type ("How to" vs "What is/are"), and keyword (e.g. "Mask). The categorized queries were plotted against time to reveal how patterns in search behavior evolved as the pandemic progressed.

Next, we had to figure out how to tell the story visually. With some projects, individual data points aren't as relevant as an aggregate value that summarizes a particular point (the total population of a city, for instance). However, in this case, the story was in the individual queries. For a query to show up in this dataset, someone, somewhere, had to enter that that exact phrase into a search. The searches, and thus these data, were personal.

We chose to represent the queries as individual circles floating across the screen, and designed the site as a single-page scrollytelling experience in which the circles would rearrange and recolor as viewers walked through the narrative. A physics engine behind the scenes enabled the circles to cluster together around key dates to show how the popularity of certain terms or categories ebbed and flowed as the pandemic evolved.

The result was a fluid animation that told a multifaceted story about the timeline of this outbreak while preserving the human-centered nature of the raw data.