BADR, in partnership with QCRI, has developed and published TweetMogaz, a system that allows Arab users to get the maximum information from the Arabic content on Twitter, on the spot.
Basically, TweetMogaz consumes streams of Arabic tweets from Twitter, classifies them into relevant topics, then present them to the users in a much more intelligent way.
By intelligence we mean that TweetMogaz can understand tweets topics’ context, group tweets based on that, and present these groups (topics) to the user for a better user experience.
TweetMogaz also is the only Arabic events detector. It’s constantly searching the Arabic content for hot, trending tweets, gathers tweets that relate and occur in a certain timeframe, then present the user a solid, homogenous story.
To achieve that feat, a thorough research has been done (and is continuously in improvment) to get the best out of the Arabic content on Twitter. The research areas extend to: Information Retrieval, Natural Language Processing, Machine Learning, Distributed Systems and Big Data.
The first publication out of TweetMogaz is a demo paper: TweetMogaz v2: Identifying News Stories in Social Media, by Eslam Elsawy (BADR), Moamen Mokhtar (BADR) and Walid Magdy (QCRI), it's published in CIKM 2014.