We knew many people's hesitancy to get the vaccine stemmed from misinformation. We wanted to see what pieces of misinformation were causing the most harm so that they could be purposefully combatted.

What it does

Our article explores the negative sentiment towards the COVID-19 vaccine on Twitter, and explains our method for identifying key pieces of misinformation.

How we built it

Our data was analyzed using Python, and the website was built using HTML and CSS and deployed using Heroku.

Challenges we ran into

Our original goal was to identify the top pieces of misinformation on social media. However, our clustering revealed that there were no significant front runners, but that mistrust largely stemmed from the public not being educated on the vaccine. This forced us to shift the focus of our article.

Accomplishments that we're proud of

We are really proud of the clustering methods we were able to accomplish, and the final look of the website.

What we learned

We learned a lot about collecting Twitter data, and analyzing that data for sentiment.

What's next for Identifying COVID-19 Misinformation in order to Combat it

Next, we would like to look into more what specific actions can be taken that would most effectively educate people on the safety of the vaccine.

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