# Characterization of citizens using word2vec and latent topic analysis in a large set of tweets

arXiv: https://arxiv.org/abs/1904.08926 · HTML: https://mlsanity.com/paper/1904.08926/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 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)

With the increasing use of the Internet and mobile devices, social networks are becoming the most used media to communicate citizens' ideas and thoughts. This information is very useful to identify communities with common ideas based on what they publish in the network. This paper presents a method to automatically detect city communities based on machine learning techniques applied to a set of tweets from Bogot'a's citizens. An analysis was performed in a collection of 2,634,176 tweets gathered from Twitter in a period of six months. Results show that the proposed method is an interesting tool to characterize a city population based on a machine learning methods and text analytics.