Hybrid Entity Driven News Detection on Twitter

In recent years, Twitter has become one of the most popular microblogging services on the Internet. People sharing their thoughts and feelings as well as the events happening around them, makes Twitter a promising source of the most recent news received directly from the observers. But detecting the newsworthy tweets is a challenging task. In this paper we propose a new hybrid method for detecting real-time news on Twitter using locality-sensitive hashing (LSH) and named-entity recognition (NER). The method is tested on 72,000 tweets from the San Fransisco area and yields a precision of 0.917.