Characterization of citizens using word2vec and latent topic analysis in a large set of tweets
This provides a tool for analyzing citizen communities based on social media data, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of automatically detecting city communities by applying machine learning techniques to a large set of tweets from Bogotá's citizens, analyzing 2,634,176 tweets over six months, and found the method to be an interesting tool for population characterization.
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.