  Cultural and social dynamics are important concepts that must be understood
in order to grasp what a community cares about. To that end, an excellent
source of information on what occurs in a community is the news, especially in
recent years, when mass media giants use social networks to communicate and
interact with their audience. In this work, we use a method to discover latent
topics in tweets from Colombian Twitter news accounts in order to identify the
most prominent events in the country. We pay particular attention to security,
violence and crime-related tweets because of the violent environment that
surrounds Colombian society. The latent topic discovery method that we use
builds vector representations of the tweets by using FastText and finds
clusters of tweets through the K-means clustering algorithm. The number of
clusters is found by measuring the $C_V$ coherence for a range of number of
topics of the Latent Dirichlet Allocation (LDA) model. We finally use Uniform
Manifold Approximation and Projection (UMAP) for dimensionality reduction to
visualise the tweets vectors. Once the clusters related to security, violence
and crime are identified, we proceed to apply the same method within each
cluster to perform a fine-grained analysis in which specific events mentioned
in the news are grouped together. Our method is able to discover event-specific
sets of news, which is the baseline to perform an extensive analysis of how
people engage in Twitter threads on the different types of news, with an
emphasis on security, violence and crime-related tweets.

Social and Information Networks;Computation and Language