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

• Published in: Cities 2019-09, Vol.92, p.187-196
• Main Authors: Vargas-Calderón, Vladimir, Camargo, Jorge E.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Citizens; Internet; Machine learning; Mass media; Natural language processing; Social network analysis; Social networks; T-sne; Word embedding
• ISSN: 0264-2751; 1873-6084
• DOI: 10.1016/j.cities.2019.03.019

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 Bogotá’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. •This paper proposes a method to automatically detect communities in the Twitter social network.•We collected a data set of tweets of Bogotá-Colombia citizens in a period of six months.•We represent the complete tweets collection using the Word2Vec model and natural language techniques.•We extract communities using a clustering algorithm to detect latent topics.•Each citizen is projected in a 2D visualization in which the obtained latent topics are colored.