iFreeq

–January 6, 1:19 p.m.–

iFreeq Main Screen

The mode, difficulty and topic selection screen.

Gameplay

The gameplay screen.

Post-game

The screen that's displayed after the game is over. It allows the ...

iFreeq is a news game designed to help educate people on important current events. The system is in two parts: one to decide which news topics are important and another to actually quiz the user.


The first part is accomplished through parsing the headlines of major newspapers (or tech blogs, or sports blogs) RSS feeds and looking for the most commonly-used nouns. This is done via Python's natural language toolkit. Once those nouns are found, it picks one of the top ten and searches Twitter for it.

The second part takes the tweets returned by Twitter and strips the chosen noun from them (as well as all extraneous formatting and URLs). It then presents those tweets two at a time to the user to try and make them guess what term is being talked about. The faster the user gets it, the more points they receive. It stores the Facebook ID of the user, so they can play against their friends and compete for the top score in each subject.

I wrote the back-end and front-end for this project, but the design was completed by a hired designer.

The server uses Tornado, an event-driven Python framework. It keeps session data in Redis and saves user data in CouchDB. It is an extremely capable server and can handle a deluge of simultaneous users (tested using Siege).





The mode, difficulty and topic selection screen.




The gameplay screen.




The screen that's displayed after the game is over. It allows the user to read the stories about the subject they just tried to guess.