COVID-19 Tweet Classification Challenge
Can you identify tweets about coronavirus without using keywords?
92 active · 451 enrolled
Good for beginners
Natural Language Processing

Meet the winners of the #ZindiWeekendz COVID-19 Tweet Classification Challenge

ML-driven sentiment analysis is an important tool to understand communities’ feelings around major issues such as COVID-19. Gathering comprehensive social data for sentiment analysis can be limited, however, if data collection relies only on keywords such as ‘coronavirus’ or ‘covid’.

The objective of this challenge is to develop a machine learning model to assess if a Twitter post is about COVID-19 or not. This model will help gather tweet data about the epidemic without relying only on key words like ‘covid’ or ‘coronavirus’ being present, allowing researchers and engineers to gather a more comprehensive dataset for sentiment analysis.

This model could be put into practice as part of a larger effort to understand online sentiment around COVID-19, and inform future communications and public interventions by governments and non-government public health organisations.


The evaluation metric for this challenge is the Area Under the Curve.

Where the value is the probability that the tweet is related to covid.

Your submission file should look like:

ID        target
test_0     0.45
test_1     0.98
test_2     0.12
test_3     0.56

This is a learning competition. Aside from knowledge, there are no prizes for this competition.

You will receive 25 points for your first submission and 50 points for your first non-sample submission. You can read more about Zindi points here.


As this is a knowledge competition it will not close.

We reserve the right to update the contest timeline if necessary.

Useful Resources

To help make this a more useful Knowledge competition, we have compiled some tutorials and other resources for learning the techniques associated with prediction. We’ll add to this list over the course of the competition, so if you have a good tutorial, a handy video or GitHub repo, or just a pro tip, post it in the discussion forums and we’ll share it here.

Read about the winner's approaches in this blog post.

Here are the top 2 solutions to this challenge.

  1. Krishna_Priya's Github repo
  2. Iheb_Bouzayani's Github repo
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As this is a learning challenge, aside from the rules in the Terms of Use, no other particular rules apply.

This challenge is open to all and not restricted to any country.

Teams and collaboration

You may participate in this competition as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the highest number of submissions among team members at team formation.

Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be disqualified.

Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).

Datasets and packages

The solution must use publicly-available, open-source packages only. Your models should not use any of the metadata provided.

You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.

If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.

You may use pretrained models as long as they are openly available to everyone.

The data used in this competition is the sole property of Zindi and the competition host. You may not transmit, duplicate, publish, redistribute or otherwise provide or make available any competition data to any party not participating in the Competition (this includes uploading the data to any public site such as Kaggle or GitHub). You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.

You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.

Your solution must not infringe the rights of any third party.

Submissions and winning

You may make a maximum of 10 submissions per day.

Note that to count, your submission must first pass processing. If your submission fails during the processing step, it will not be counted and not receive a score; nor will it count against your daily submission limit. If you encounter problems with your submission file, your best course of action is to ask for advice on the Competition’s discussion forum.

Note that there is no public/private leaderboard split for this challenge. Read more about public and private leaderboards in this post.

You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.

Zindi also reserves the right to disqualify you and/or your submissions from any competition if we believe that you violated the rules or violated the spirit of the competition or the platform in any other way. The disqualifications are irrespective of your position on the leaderboard and completely at the discretion of Zindi.

Please refer to the FAQs and Terms of Use for additional rules that may apply to this competition. We reserve the right to update these rules at any time.