News professionals are facing the challenge of discovering news from more diverse and unreliable information in the age of social media. More and more news events break on social media first and are picked up by news media subsequently. The recent Brussels attack is such an example. At Reuters, a global news agency, we have observed the necessity of providing a more effective tool that can help our journalists to quickly discover news on social media, verify them and then inform the public.   In this paper, we describe  Reuters Tracer , a system for sifting through all noise to detect news events on Twitter and assessing their veracity. We disclose the architecture of our system and discuss the various design strategies that facilitate the implementation of machine learning models for noise filtering and event detection. These techniques have been implemented at large scale and successfully discovered breaking news faster than traditional journalism
['Xiaomo Liu', 'Quanzhi Li', 'Armineh Nourbakhsh', 'Rui Fang', 'Merine Thomas', 'Kajsa Anderson', 'Russ Kociuba', 'Mark Vedder', 'Steven Pomerville', 'Ramdev Wudali', 'Robert Martin', 'John Duprey', 'Arun Vachher', 'William M. Keenan', 'Sameena Shah']
Reuters Tracer: A Large Scale System of Detecting & Verifying Real-Time News Events from Twitter