Emerging topics in Brexit debate on Twitter around the deadlines a probabilistic topic modelling approach
File(s)
Published version
Author(s)
del Gobbo, Emiliano
Fontanella, Sara
Sarra, Annalina
Fontanella, Lara
Type
Journal Article
Abstract
The present study is focused on the online debate relating to the Brexit process, three years and half since the historical referendum that has sanctioned the divide of the United Kingdom from the European Union. In our analysis we consider a corpus of approximately 33 million Brexit related tweets, shared on Twitter for 58 weeks, spanning from 31 December 2019 to 9 February 2020. Due to its great accessibility to data, Twitter constitutes a convenient data source to monitor and evaluate a wide variety of topics. In addition, Twitter’s marked orientation towards news and the dissemination of information makes this microblogging network more connected to politics compared to other platforms. Through static and dynamic topic modelling techniques, we were able to identify the topics that have attracted the most attention from Twitters users and to characterise their temporal evolution. The topics retrieved by the static model highlight the major events of the Brexit process while the dynamic analysis recovered the persistent themes of discussion and debate over the entire period.
Date Issued
2020-07-22
Date Acceptance
2020-07-12
Citation
Social Indicators Research: an international and interdisciplinary journal for quality-of-life measurement, 2020, 156, pp.669-688
ISSN
0303-8300
Publisher
Springer
Start Page
669
End Page
688
Journal / Book Title
Social Indicators Research: an international and interdisciplinary journal for quality-of-life measurement
Volume
156
Copyright Statement
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000551411200003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Social Sciences
Social Sciences, Interdisciplinary
Sociology
Social Sciences - Other Topics
Social media
Twitter data
Brexit
Topic models
Latent Dirichlet Allocation
AGENDA
MEDIA
SCIENCE
Publication Status
Published
Date Publish Online
2020-07-22