  We propose a method to discover latent topics and visualise large collections
of tweets for easy identification and interpretation of topics, and exemplify
its use with tweets from a Colombian mass media giant in the period 2014--2019.
The latent topic analysis is performed in two ways: with the training of a
Latent Dirichlet Allocation model, and with the combination of the FastText
unsupervised model to represent tweets as vectors and the implementation of
K-means clustering to group tweets into topics. Using a classification task, we
found that people respond differently according to the various news topics. The
classification tasks consists of the following: given a reply to a news tweet,
we train a supervised algorithm to predict the topic of the news tweet solely
from the reply. Furthermore, we show how the Colombian peace treaty has had a
profound impact on the Colombian society, as it is the topic in which most
people engage to show their opinions.
