Skip to Main content Skip to Navigation
Journal articles

Categorizing Air Quality Information Flow on Twitter Using Deep Learning Tools

Abstract : Environmental health is an emerging and hotly debated topic that covers several fields of study such as pollution in urban or rural environments and the consequences of these changes on health populations. In this field of intersectorial forces, the complexity of stakeholders’ logics is realized in the production, use and communication of data and information on air quality. The Twitter platform is a `` partial public space '' that can throw light on the different types of stakeholders involved, the information and issues discussed and the dynamics of articulation between these different aspects. A methodology aiming 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, is presented. To achieve this, we developed 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.
Complete list of metadata

Cited literature [18 references]  Display  Hide  Download

https://halshs.archives-ouvertes.fr/halshs-01800457
Contributor : Jean-Luc Minel Connect in order to contact the contributor
Submitted on : Wednesday, September 5, 2018 - 11:16:00 AM
Last modification on : Wednesday, November 3, 2021 - 9:49:44 AM
Long-term archiving on: : Thursday, December 6, 2018 - 2:22:43 PM

File

ICCIFinalDraft.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : halshs-01800457, version 1

Citation

Brigitte Juanals, Jean-Luc Minel. Categorizing Air Quality Information Flow on Twitter Using Deep Learning Tools. Lecture Notes in Artificial Intelligence, Springer, 2018, Computational Collective Intelligence, 11055, pp.109-118. ⟨halshs-01800457⟩

Share

Metrics

Record views

388

Files downloads

443