  Citizens are actively interacting with their surroundings, especially through
social media. Not only do shared posts give important information about what is
happening (from the users' perspective), but also the metadata linked to these
posts offer relevant data, such as the GPS-location in Location-based Social
Networks (LBSNs). In this paper we introduce a global analysis of the
geo-tagged posts in social media which supports (i) the detection of unexpected
behavior in the city and (ii) the analysis of the posts to infer what is
happening. The former is obtained by applying density-based clustering
techniques, whereas the latter is consequence of applying natural language
processing. We have applied our methodology to a dataset obtained from
Instagram activity in New York City for seven months obtaining promising
results. The developed algorithms require very low resources, being able to
analyze millions of data-points in commodity hardware in less than one hour
without applying complex parallelization techniques. Furthermore, the solution
can be easily adapted to other geo-tagged data sources without extra effort.
