the literature to explore events. Trend Miner [@preoctiuc2013temporal] extracts multilingual terms from social media, groups and visualizes them in temporal series. Social Sensor [@aiello2013sensing] and Twitcident [@abel2012twitcident] may be the most similar systems to ours. The first one focuses on tracking topic or events predefined by the user. The second makes user defined searches related to crisis management. LRA[^1] aims to discovering and tracking crisis situations based on crowdsourced information. ReDites [@osborne2014real] detects an tracks topics in a fully automated way.

Detecting terms that represent a domain or topic semantically has been traditionally addressed by statistical models such as Latent Dirichlet Association (LDA) [@blei2003latent]. Classical LDA models are applied over static document collections. In order to extract terms from dynamic collections, the most common approach is to follow a two step strategy [@shamma2011peaks] consisting of detecting emerging terms and grouping them in clusters thereby defining a domain.

@nguyen2016hot predict emerging terms by means of word co-occurrence distributional models, comparing the terms in an specific time window against the whole collection. @abilhoa2014keyword use a graph-based representation of the document collection. @aiello2013sensing propose $df$-$idf_t$ (Document Frequency - Inverse Document Frequency), a variation of $tf$-$idf$ that includes the temporal factor. @kim2016exploring combine neural 