Real Disaster Tweets Prediction
Predict if a tweet is really reporting a disaster or not
Real Disaster Tweets Prediction| Mar, 2023 | Kaggle Notebook | Github
Twitter has become an important communication channel in times of emergency.
The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies).
But, it’s not always clear whether a person’s words are actually announcing a disaster. Take this example:
The author explicitly uses the word “ABLAZE” but means it metaphorically. This is clear to a human right away, especially with the visual aid. But it’s less clear to a machine.
In this competition, we’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t.
Using the Transformer encoder model, I initially acheived a 0.75758 accuracy. Upon fine-tuning, the accuracy is improved to 0.78577.
If you are interested in discussions about how I did this, I have a blog detailed my solution, part 1, part 2 to this problem.