Utilising novel data streams in the fight against COVID-19

With social distancing measures in place, a large amount of discourse relating to COVID-19 now takes places on social media platforms such as Twitter.

With social distancing measures in place, a large amount of discourse relating to COVID-19 now takes places on social media platforms such as Twitter. These platforms contain a treasure trove of information that can help us answer questions such as how many people are exhibiting Coronavirus symptoms today? However, not all information is created equal — these platforms also contain a lot of misinformation which could potentially cause harm to members of the public.

We developed a system to track and analyse tweets that mention symptoms of COVID-19. This system ‘listens’ for tweets that mention COVID-19 symptoms. Once identified, tweets are fed through a machine learning classifier which identifies whether it relates to a user’s personal symptoms, someone else’s symptoms or if the tweet contains misinformation.

We can also use geolocation data to calculate the number of users who tweet about symptoms in each region of a given country (where geolocation is permitted by the user). From this data, it is possible to determine the number of users who travel between different regions of a given country. This information could potentially help to identify new outbreak clusters within a country and provide insight into how members of the public responded to lockdown measures.

covid19 machine-learning datastream data-science coronavirus data analysis

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