Our group is passionate about environmental issues and wanted to create a way to aid those who have been affected by natural disasters. We also wanted to ease the difficulty that first responders have in aiding people in disaster relief. We know social media contains lots of helpful information with this, so we created a way to compile and analyze this data.
What it does
Using location-tagged social media posts (eg. tweets), our app analyzes the conditions of different areas affected by natural disasters. This data is analyzed through Google's NLP, indicating the disaster level of different areas, and this data is plotted with a location tag on our app
How I built it
We used Figma to create the app demo, python to use Google's NLP, and java to mock the information that tweets would contain and how to map the location of our analyzed tweets.
Challenges I ran into
We found it hard to use the NLP and API because we haven't ever done anything like this before. We were also unsure of how to access information from social media (eg. tweets), but the mentors helped us overcome these obstacles.
Accomplishments that I'm proud of
We are proud of all the code we have been able to create! Since this is our first hackathon, we're also really proud of creating an idea that could be developed in the future to help those in need who have been affected by natural disasters.
What's next for Post For Good
Some ideas we have for the future are to: create a web-app to reach more users, gather data from multiple social media sources (eg. Snapchat), and to use news sources or another NLP to fact-check our data. For our code, we could also train our own API to recognize the severity of disaster areas on it’s own, gain a Twitter User Access Token to access and analyze tweets in real-time, and better develop our app’s desired features to work on a real phone.