An instrumented methodology to analyze and categorize information flows on twitter using nlp and deep learning: A use case on air quality

Abstract

This article focuses on the development of an instrumented methodology for modeling and analyzing the circulation message flows concerning air quality on the social network Twitter. This methodology aims at describing and representing, on the one hand, the modes of circulation and distribution of message flows on this social media and, on the other hand, the content exchanged between stakeholders. To achieve this, we developed Natural Language Processing (NLP) tools and a classifier based on Deep Learning approaches in order to categorize messages from scratch. The conceptual and instrumented methodology presented is part of a broader interdisciplinary methodology, based on quantitative and qualitative methods, for the study of communication in environmental health. A use case of air quality is presented.