No Cover Image

Book Chapter 52 views

Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study / Nabeel Albishry; Tom Crick; Tesleem Fagade; Theo Tryfonas

Computational Collective Intelligence, Volume: 11055

Swansea University Author: Crick, Tom

  • Accepted Manuscript under embargo until: 8th August 2019

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...

Full description

Published in: Computational Collective Intelligence
ISBN: 978-3-319-98442-1 978-3-319-98443-8
ISSN: 0302-9743 1611-3349
Published: Bristol, UK Springer 2018
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa43571
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2018-08-27T13:39:16Z
last_indexed 2018-10-09T19:34:56Z
id cronfa43571
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2018-10-09T16:13:58Z</datestamp><bib-version>v2</bib-version><id>43571</id><entry>2018-08-27</entry><title>Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study</title><alternativeTitle></alternativeTitle><author>Tom Crick</author><firstname>Tom</firstname><surname>Crick</surname><active>true</active><ORCID>0000-0001-5196-9389</ORCID><ethesisStudent>false</ethesisStudent><sid>200c66ef0fc55391f736f6e926fb4b99</sid><email>9971fd6d74987b78a0d7fce128f8c721</email><emailaddr>z93Ri4T5hwMLTfh+6XG11n2HZhUyFASdV1DFdgIIhKs=</emailaddr><date>2018-08-27</date><deptcode>EDUC</deptcode><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 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&#x2013;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.</abstract><type>Chapter in book</type><journal>Computational Collective Intelligence</journal><volume>11055</volume><journalNumber/><paginationStart/><paginationEnd>177</paginationEnd><publisher>Springer</publisher><placeOfPublication>Bristol, UK</placeOfPublication><isbnPrint>978-3-319-98442-1</isbnPrint><isbnElectronic>978-3-319-98443-8</isbnElectronic><issnPrint>0302-9743</issnPrint><issnElectronic>1611-3349</issnElectronic><keywords>Trends, topic spread, popularity, network graphs, Twitter</keywords><publishedDay>5</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2018</publishedYear><publishedDate>2018-09-05</publishedDate><doi>10.1007/978-3-319-98443-8_16</doi><url>https://link.springer.com/chapter/10.1007%2F978-3-319-98443-8_16</url><notes>Proceedings of 10th International Conference on Computational Collective Intelligence (ICCCI 2018)</notes><college>College of Arts and Humanities</college><department>School of Education</department><CollegeCode>CAAH</CollegeCode><DepartmentCode>EDUC</DepartmentCode><institution/><researchGroup>None</researchGroup><supervisor/><sponsorsfunders/><grantnumber/><degreelevel/><degreename>None</degreename><lastEdited>2018-10-09T16:13:58Z</lastEdited><Created>2018-08-27T09:18:16Z</Created><path><level id="1">College of Science</level><level id="2">Computer Science</level></path><authors><author><firstname>Nabeel</firstname><surname>Albishry</surname><orcid/><order>1</order></author><author><firstname>Tom</firstname><surname>Crick</surname><orcid>0000-0001-5196-9389</orcid><order>2</order></author><author><firstname>Tesleem</firstname><surname>Fagade</surname><orcid/><order>3</order></author><author><firstname>Theo</firstname><surname>Tryfonas</surname><orcid>0000-0003-4024-8003</orcid><order>4</order></author></authors><documents><document><filename>Under embargo</filename><originalFilename>Under embargo</originalFilename><uploaded>2018-08-27T09:19:22Z</uploaded><type>Output</type><contentLength>313280</contentLength><contentType>application/pdf</contentType><version>AM</version><cronfaStatus>true</cronfaStatus><action>Updated Copyright</action><actionDate>09/10/2018</actionDate><embargoDate>2019-08-08T00:00:00</embargoDate><documentNotes/><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents></rfc1807>
spelling 2018-10-09T16:13:58Z v2 43571 2018-08-27 Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study Tom Crick Tom Crick true 0000-0001-5196-9389 false 200c66ef0fc55391f736f6e926fb4b99 9971fd6d74987b78a0d7fce128f8c721 z93Ri4T5hwMLTfh+6XG11n2HZhUyFASdV1DFdgIIhKs= 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. Chapter in book Computational Collective Intelligence 11055 177 Springer Bristol, UK 978-3-319-98442-1 978-3-319-98443-8 0302-9743 1611-3349 Trends, topic spread, popularity, network graphs, Twitter 5 9 2018 2018-09-05 10.1007/978-3-319-98443-8_16 https://link.springer.com/chapter/10.1007%2F978-3-319-98443-8_16 Proceedings of 10th International Conference on Computational Collective Intelligence (ICCCI 2018) College of Arts and Humanities School of Education CAAH EDUC None None 2018-10-09T16:13:58Z 2018-08-27T09:18:16Z College of Science Computer Science Nabeel Albishry 1 Tom Crick 0000-0001-5196-9389 2 Tesleem Fagade 3 Theo Tryfonas 0000-0003-4024-8003 4 Under embargo Under embargo 2018-08-27T09:19:22Z Output 313280 application/pdf AM true Updated Copyright 09/10/2018 2019-08-08T00:00:00 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
Crick, Tom
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_***_Crick, Tom
author Crick, Tom
author2 Nabeel Albishry
Tom Crick
Tesleem Fagade
Theo Tryfonas
format Book Chapter
container_title Computational Collective Intelligence
container_volume 11055
publishDate 2018
institution Swansea University
isbn 978-3-319-98442-1
978-3-319-98443-8
issn 0302-9743
1611-3349
doi_str_mv 10.1007/978-3-319-98443-8_16
publisher Springer
college_str College of Science
hierarchytype
hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
url https://link.springer.com/chapter/10.1007%2F978-3-319-98443-8_16
document_store_str 0
active_str 1
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 2018-09-05T06:20:43Z
_version_ 1639462881821982720
score 10.827389