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Conference Paper/Proceeding/Abstract 21711 views 139 downloads

Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study

Nabeel Albishry, Tom Crick Orcid Logo, Tesleem Fagade, Theo Tryfonas

Computational Collective Intelligence, Volume: 11055, Pages: 167 - 177

Swansea University Author: Tom Crick Orcid Logo

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DOI (Published version): 10.1007/978-3-319-98443-8_16

Abstract

Thousands of topics trend on Twitter across the world every day, making it increasingly challenging to provide real-time analysis of current issues, topics and themes being discussed across various locations and jurisdictions. There is thus a demand for simple and extensible approaches to provide de...

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Published in: Computational Collective Intelligence
ISBN: 9783319984421 9783319984438
ISSN: 0302-9743 1611-3349
Published: Cham Springer International Publishing 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa43571
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first_indexed 2018-08-27T13:39:16Z
last_indexed 2023-01-11T14:20:13Z
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spelling 2022-12-18T17:29:29.1899528 v2 43571 2018-08-27 Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study 200c66ef0fc55391f736f6e926fb4b99 0000-0001-5196-9389 Tom Crick Tom Crick true false 2018-08-27 EDUC Thousands of topics trend on Twitter across the world every day, making it increasingly challenging to provide real-time analysis of current issues, topics and themes being discussed across various locations and jurisdictions. There is thus a demand for simple and extensible approaches to provide deeper insight into these trends and how they propagate across locales. This paper represents one of the first studies to look at geospatial spread of trends on Twitter, presenting various techniques to provide increased understanding of how trends on social networks can spread across various regions and nations. It is based on a year-long data collection (N=2,307,163) and analysis between 2016–2017 of seven Middle Eastern countries (Bahrain, Egypt, Kuwait, Lebanon, Qatar, Saudi Arabia, and the United Arab Emirates). Using this year-long dataset, the project investigates the popularity and geospatial spread of trends, focusing on trend information but not processing individual topics, with the findings showing that likelihood of trends spreading to other locales is to a large extent influenced by the place in which it first appeared. Conference Paper/Proceeding/Abstract Computational Collective Intelligence 11055 167 177 Springer International Publishing Cham 9783319984421 9783319984438 0302-9743 1611-3349 Trends, topic spread, popularity, network graphs, Twitter 8 8 2018 2018-08-08 10.1007/978-3-319-98443-8_16 Proceedings of 10th International Conference on Computational Collective Intelligence (ICCCI 2018) COLLEGE NANME Education COLLEGE CODE EDUC Swansea University 2022-12-18T17:29:29.1899528 2018-08-27T09:18:16.3163931 Faculty of Humanities and Social Sciences School of Social Sciences - Education and Childhood Studies Nabeel Albishry 1 Tom Crick 0000-0001-5196-9389 2 Tesleem Fagade 3 Theo Tryfonas 4 0043571-27082018091922.pdf iccci2018_paper63_cameraready.pdf 2018-08-27T09:19:22.2270000 Output 313280 application/pdf Accepted Manuscript true 2019-08-08T00:00:00.0000000 true eng
title Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study
spellingShingle Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study
Tom Crick
title_short Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study
title_full Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study
title_fullStr Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study
title_full_unstemmed Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study
title_sort Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study
author_id_str_mv 200c66ef0fc55391f736f6e926fb4b99
author_id_fullname_str_mv 200c66ef0fc55391f736f6e926fb4b99_***_Tom Crick
author Tom Crick
author2 Nabeel Albishry
Tom Crick
Tesleem Fagade
Theo Tryfonas
format Conference Paper/Proceeding/Abstract
container_title Computational Collective Intelligence
container_volume 11055
container_start_page 167
publishDate 2018
institution Swansea University
isbn 9783319984421
9783319984438
issn 0302-9743
1611-3349
doi_str_mv 10.1007/978-3-319-98443-8_16
publisher Springer International Publishing
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Social Sciences - Education and Childhood Studies{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Social Sciences - Education and Childhood Studies
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description Thousands of topics trend on Twitter across the world every day, making it increasingly challenging to provide real-time analysis of current issues, topics and themes being discussed across various locations and jurisdictions. There is thus a demand for simple and extensible approaches to provide deeper insight into these trends and how they propagate across locales. This paper represents one of the first studies to look at geospatial spread of trends on Twitter, presenting various techniques to provide increased understanding of how trends on social networks can spread across various regions and nations. It is based on a year-long data collection (N=2,307,163) and analysis between 2016–2017 of seven Middle Eastern countries (Bahrain, Egypt, Kuwait, Lebanon, Qatar, Saudi Arabia, and the United Arab Emirates). Using this year-long dataset, the project investigates the popularity and geospatial spread of trends, focusing on trend information but not processing individual topics, with the findings showing that likelihood of trends spreading to other locales is to a large extent influenced by the place in which it first appeared.
published_date 2018-08-08T03:54:49Z
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score 10.998002

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