Does this tweet contain self-reported COVID19 symptoms?

Symptoms can be about the person tweeting or someone they are referring to directly. They should be current and not about past events. If you are unsure about your annotation, hit the “skip” button.”

L’application #StopCovid elle est facile à utiliser : il suffit de se rappeler qu’elle fonctionne avec le Blue Tousse.

June 16, 2020

Yes No skip

What is this about?

In this study, we aim to create a model of how tweets about self-reported COVID19 symptoms can help predict upcoming pandemic waves, and more generally the rise and fall of the disease. To that end, we crawled public tweets from the Paris region filtered by symptoms keywords, and plotted them in time (see the graph below).

However, this filtering is very crude, e.g people don't only tweet about symptoms when they are currently falling sick, but also about that one time a year ago when they fell sick, or when talking about the general news.

To filter out such false-positives we need your help! Which of these tweets are describing an acute symptom and which ones don't? Your contribution will make a direct impact!

If you want to learn more about the people behind this project you can visit our About page.

Latest annotations

Tweet Annotation date
@mention Qu’il se pointe dans un hôpital avec une toux. Ce serait drôle qu’il arrive à la fin d’1 semaine d’un-e IDE finissant ses 35HRS... « désolé, Monsieur, je suis payé à l’heure. Au revoir Monsieur.» #tolerancezero #balancetonconnard #pasdedeontologiepourlescons #Deconfinementjour1 [url] skip Feb. 28, 2021, 7:52 p.m.
#Coronavirus  : l’urgence absolue de créer des structures de prise en charge des patients peu symptomatiques — via @mention [url] yes Feb. 28, 2021, 9:02 a.m.
@mention @mention Attention, c’est un symptôme d’infection ! yes Feb. 28, 2021, 9:02 a.m.
Cest des baiser a paris , tu tousse une fois dans ta main dans le metro TOUS LE MLNDE SELOIGNE DE TOI faut arrêter la parano la oh yes Feb. 28, 2021, 9:02 a.m.
@mention " les parents " ... Tousse, tousse...🤡 [url] yes Feb. 28, 2021, 9:01 a.m.

Do you want to run your own data analyses?

You can download all 7919 annotations (including a UUID referring to each annotator session) to create your own filtering or machine learning algorithm. Please visit our GitHub repository if you want to contribute!

Download Tweets Download Annotation


Below we visualise the number of tweets containing COVID19 symptoms using 7 days averaging windows, both using our simple filtering system (orange), or the improved filtering using crowd-sourced annotation (blue). To build that curve, we only take into account tweets that have been labelled as "yes" at least 50% of the time ; we then show the daily rate of tweets labelled as "yes" multiplied by the daily number of tweets containing symptoms.

We show as a reference the number of passages to emergencies (see Analysis for more information).