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.”

@mention @mention Je suis d’accord avec toi mou@mentionême ça le choque quand elle renifle elle tousse sans mettre le coude oui j’ai droit de dire ça fait pas de moi une méchante

May 5, 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 @mention et une agréable fièvre pour nous 😂 skip Nov. 25, 2021, 4:03 a.m.
@mention Rien. Du Doliprane si fièvre. yes Nov. 16, 2021, 4:05 p.m.
@mention Ba ouais c’est un symptôme yes Nov. 16, 2021, 4:05 p.m.
@mention Si tu touches avec les mains le banc à l'endroit où quelqu'un a postilloné et toussé, puis que tu te touches le visage, tu es contaminé. Ce n'est pas si rare comme geste. yes Nov. 16, 2021, 4:05 p.m.
@mention @mention Respirez sans tousser bande de crétins ! yes Nov. 16, 2021, 4:05 p.m.

Do you want to run your own data analyses?

You can download all 8613 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

Visualization

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).