An Instrumented Methodology to Analyze and Categorize Information Flows on Twitter Using NLP and Deep Learning : A Use Case on Air Quality 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.

A Taxonomy for Classifying User Group Activity in Online Political Discourse Twitter has become a popular platform for political conversation. Within the TwitterSphere, multiple users contribute to the discourse through different modalities and with varying degrees of engagement. In this paper, we seek to profile the different groups of users present in online political discourse. We provide a taxonomy that can be used to classify the various users, determine each groupu0027s representation and their respective share of the online conversation. Along with core information foraging concepts, the taxonomy aims to reduce the foraging costs associated with exploratory searching. This work provides a practical approach for analyzing and preprocessing social media data and offers valuable recommendation for future expansion into sentiment analysis.