Ifrim, GeorgianaGeorgianaIfrimShi, BichenBichenShiBrigadir, IgorIgorBrigadir2016-04-052016-04-052014 ACM2014-04-08http://hdl.handle.net/10197/7546Second Workshop on Social News on the Web (SNOW), Seoul, Korea, 8 April 2014Twitter has become as much of a news media as a social network, and much research has turned to analysing its content for tracking real-world events, from politics to sports and natural disasters. This paper describes the techniques we employed for the SNOW Data Challenge 2014, described in [16]. We show that aggressive lettering of tweets based on length and structure, combined with hierarchical clustering of tweets and ranking of the resulting clusters, achieves encouraging results. We present empirical results and discussion for two different Twitter streams focusing on the US presidential elections in 2012 and the recent events about Ukraine, Syria and the Bitcoin, in February 2014.en© 2014 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in http://www.snow-workshop.org/2014/.Event detectionTwitterSocial mediaDigital journalismNews aggregationConference Publication2014-10-14https://creativecommons.org/licenses/by-nc-nd/3.0/ie/