en
Doctoral thesis
Open access
English

DelibAnalysis: understanding online deliberation through automated discourse quality analysis and topic modeling

Defense date2018-06-22
Abstract

The thesis examines political discourse quality online and proposes a methodology for analyzing online conversations in an automated way. The study builds on Habermas' work by examining the quality of the public sphere in a digital age. Primarily, it examines the portion of the public sphere which deals with political discussions on online platforms. The proposed technique, DelibAnalysis, is a combination of random forests classification and k-means clustering using term-frequency inverse-document-frequency. The DelibAnalysis methodology is applied to a diverse dataset of online conversations between citizens and elected representatives in Canada, the United States and the United Kingdom using Facebook and blog platforms. This analysis is used to derive insights about the state of the online public sphere and the differences between platforms and discussion frameworks. The objective of this research is to provide a systematic framework for the semi-automated discourse quality analysis of large datasets, and in applying this framework, to yield insight into the structure and features of political discussions online.

eng
Keywords
  • Machine learning
  • Deliberative democracy
  • Discourse quality
  • Habermas
  • Computational analysis
  • Online deliberation
  • Social media
Citation (ISO format)
FOURNIER-TOMBS, Eléonore. DelibAnalysis: understanding online deliberation through automated discourse quality analysis and topic modeling. 2018. doi: 10.13097/archive-ouverte/unige:112458
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Technical informations

Creation07/18/2018 4:23:00 PM
First validation07/18/2018 4:23:00 PM
Update time03/15/2023 3:18:22 PM
Status update03/15/2023 3:18:22 PM
Last indexation05/03/2024 6:53:50 AM
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