networks and sequence labelling in order to extract relevant terms from conversations. @miao2017cost propose to reduce the cost of predicting emerging topics by finding a small group of representative users and predict the emerging topics from their social media activity.

There is also the problem of the scope of the event or topic to be tracked. An event might be tracked at global level (e.g. Football World Cup), but most events are local or regional at most. Two issues arise at this point. Firstly, how to restrict the data gathered to a specific region, and, secondly, how to cope with multilingual data. Some authors tackle the problem by automatically geolocating tweets while others try focus on user locations. See @jurgens2015geolocation [@zubiaga2017towards] for reviews of previous approaches. Our approach is to geolocate users rather than tweets, in order to construct a census of Twitter users in a region. We developed a SVM classifier similar to @zubiaga2017towards using follower and friend information as features.

Sentiment Analysis {#isv2018:soa:SA}
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In the last years microblogging sites such as Twitter have attracted the attention of many researchers with diverse objectives such as stock market prediction [@bollen_twitter_2010; @oliveira2017impact], polling estimation [@oconnor_tweets_2010; @ceron2015using] or analysis of crisis 