UNIGE document Doctoral Thesis
previous document  unige:112458  next document
add to browser collection

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

Defense Thèse de doctorat : Univ. Genève, 2018 - SDS 94 - 2018/06/22
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.
Keywords Machine learningDeliberative democracyDiscourse qualityHabermasComputational analysisOnline deliberationSocial media
URN: urn:nbn:ch:unige-1124585
Full text
Thesis (3.6 MB) - public document Free access
Dataset: https://github.com/eleonoreft/DelibAnalysis
Research group Institute of Information Service Science (ISS)
(ISO format)
FOURNIER-TOMBS, Eléonore. DelibAnalysis: understanding online deliberation through automated discourse quality analysis and topic modeling. Université de Genève. Thèse, 2018. doi: 10.13097/archive-ouverte/unige:112458 https://archive-ouverte.unige.ch/unige:112458

297 hits



Deposited on : 2018-12-19

Export document
Format :
Citation style :