In 2006, 98.75% of tweets were in English. Since then, that percentage has been going down. Today, 49% of tweets are not in the English language, and 78% of Twitter users are non-American. These statistics implies the trend of Twitter's globalization, a major factor of which is the use of service to discuss and increase awareness about major global events. When major global events occur, the amount of non-American users rises rapidly. Currently, most Twitter Analytics presents data only taken from the U.S., but we believe it is important for Twitter users in non-english speaking countries to search what is being talked about through their own language. Because of this, we wanted to utilize data visualization and analytics to create an app that allows users to search usernames and keywords in their own languages that gives them the user's influence for those keywords in those languages.
What it does
Determines the influence of a Twitter user on a specific topic based on keywords in user and followers' tweets.
How we built it
We used Android Studio and java to built the front end and backend we used java and the Twitter API.
Challenges we ran into
Linking the backend with the front end, we are getting weird errors with threading.
Accomplishments that we're proud of
We learned completely new skill sets such as Android Studio, API calls, and app development. And finally connecting most of the app, although we couldn't get the last step
What we learned
Most of us are first-time hackers, so it took us hours to even come up with a viable and meaningful idea. Next time, we should have a couple practical ideas in mind beforehand to vote on when the Hackathon actually comes. Next time, we should also push commits to Github beforehand to pull each person's activity from, instead of doing individual parts and then trying to merge them and make them compatible.
What's next for tweetLadder
We want to finish the merging of the front end and back end of the app. We also want to find a way to bypass the Twitter API rate limits to reach a bigger data set to analyze.