COVID-19 Digital Observatory

This project page is currently under construction.

Microscopy image of the virus which causes COVID-19.

This page documents a digital observatory project that aims to collect, aggregate, distribute, and document public social data from digital communication platforms in relation to the 2019–20 coronavirus pandemic . The project is being coordinated by the Community Data Science Collective and Pushshift.

Overview and objectives

As people struggle to make sense of the COVID-19 pandemic, many of us turn to social media and social computing systems to share information, to understand what's happening, and to find new ways to support one another. We are building a digital observatory to understand where and how people are talking about COVID-19-related topics. The observatory collects, aggregates, and distributes social data related to how people are responding to the ongoing public health crisis of COVID-19. The public datasets and freely licensed tools, techniques, and knowledge created through this project will allow researchers, practitioners, and public health officials to more efficiently gather, analyze, understand, and act to improve these crucial sources of information during crises.

Everything here is a work in progress as we get the project running, create communication channels, and start releasing datasets. Learn how you can stay connected, use our resources as we produce them, and get involved below.

Stay connected

Subscribe to our low traffic announcement mailing list. You can fill out the form on the list website or email covid19-announce-request@communitydata.science with the word 'help' in the subject or body (don't include the quotes). You will get back a message with instructions.

The email list will contain occasional updates, information about new datasets, partnerships, and so on.

Access Data

(coming soon)

Our initial data releases from this project provide a good starting point for investigating social computing and social media content related COVID-19. At first, we will focus on providing static datasets from Wikipedia and related Wikimedia projects as well as search engine results pages (SERPs) for a set of searches on COVID-19 relevant terms. Over time, we plan to include more sources of data (including international/non-English information), localized content specific to particular geographic regions, additional social media and social computing data sources, and searches from more keywords, all delivered more quickly.

Search Engine Results Pages (SERP) Data

The SERP data in our initial data release includes the first search result page from Google and Bing for a variety of COVID-19 related terms gathered from Google Trends and Google and Bing's autocomplete "search suggestions." Specifically, using a set of six "stem keywords" about COVID-19 and online communities ("coronavirus", "coronavirus reddit", coronavirus wiki", "covid 19", "covid 19 reddit", and "covid 19 wiki"), we collected related keywords from Google Trends (using open source software https://www.npmjs.com/package/google-trends-api) and autcomplete suggestions from Google and Bing (using open source software https://github.com/gitronald/suggests).

<TODO quick overview format/structure in which data is distributed> Within these compressed archives, each SERP has three data files: a PNG screenshot of the full first page of results, an mhtml "snapshot" (https://github.com/puppeteer/puppeteer/issues/3658), and a json file with a variety of metadata (e.g. date, the device emulated) and a list of every link (<a>) element in the page with its coordinates (top, left, bottom, right) in pixels.

Keywords and search terms

We currently use and provide three different types of keywords and search terms:

• Article names/topics from Wikipedia's WikiProject Covid-19
• Wikidata entities generated via the "Main items" described by Wikidata's WikiProject COVID-19
• Top 25 daily trending search terms from Google and Bing.

Get Help Using Data

(coming soon)

We will also develop tutorials and demos for the use of the data we release. In some cases, the data sources are quite large and might not be suitable for analysis on your personal computer. Wherever possible, we'll try to point you to useful tools to help make this feasible or easier.

Contribute

We are eager for collaborators and committed to working openly.

• Want to contribute to this wiki? Consider creating an account and be bold!
• Want to contribute to our datasets and/or analysis code? Clone our github repository and pitch in with pull requests.

Code of Conduct

We ask that all contributors adhere to the Contributor Covenant.