# Using machine learning and information visualisation for discovering latent topics in Twitter news

arXiv: https://arxiv.org/abs/1910.09114 · HTML: https://mlsanity.com/paper/1910.09114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index](https://mlsanity.com/paper/2111.00057/) (2021)

## Abstract (arXiv)

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.