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

Nabeel Albishry; Tom Crick; Tesleem Fagade; Theo Tryfonas

doi:10.1007/978-3-319-98443-8_16

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

Nabeel Albishry, Tom Crick, Tesleem Fagade, Theo Tryfonas

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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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 2016–2017 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.

Original language	English
Title of host publication	Proceedings of the 10th International Conference on Computational Collective Intelligence (ICCCI 2018)
Subtitle of host publication	5-7 September 2018 - Bristol, UK
Publisher	Springer
Pages	167-177
Number of pages	11
Volume	11055
ISBN (Electronic)	978-3-319-98443-8
ISBN (Print)	978-3-319-98442-1
DOIs	https://doi.org/10.1007/978-3-319-98443-8_16
Publication status	Published - Sept 2018
Event	10th International Conference on Computational Collective Intelligence - University of the West of England, Bristol, United Kingdom Duration: 5 Sept 2018 → 7 Sept 2018

Publication series

Name	Lecture Notes in Computer Science
ISSN (Print)	0302-9743

Conference

Conference	10th International Conference on Computational Collective Intelligence
Abbreviated title	ICCCI 2018
Country/Territory	United Kingdom
City	Bristol
Period	5/09/18 → 7/09/18

Keywords

Trends
Topic spread
Popularity
Network graphs
Twitter

Access to Document

10.1007/978-3-319-98443-8_16

Full-text PDF (accepted author manuscript)
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Springer at https://link.springer.com/chapter/10.1007%2F978-3-319-98443-8_16#copyrightInformation . Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 279 KB

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HPC (High Performance Computing) and HTC (High Throughput Computing) Facilities
Alam, S. R. (Manager), Williams, D. A. G. (Manager), Eccleston, P. E. (Manager) & Greene, D. (Manager)
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Cite this

Albishry, N., Crick, T., Fagade, T., & Tryfonas, T. (2018). Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study. In Proceedings of the 10th International Conference on Computational Collective Intelligence (ICCCI 2018): 5-7 September 2018 - Bristol, UK (Vol. 11055, pp. 167-177). Article 63 (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-98443-8_16

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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 2016–2017 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.",

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Albishry, N, Crick, T, Fagade, T & Tryfonas, T 2018, Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study. in Proceedings of the 10th International Conference on Computational Collective Intelligence (ICCCI 2018): 5-7 September 2018 - Bristol, UK. vol. 11055, 63, Lecture Notes in Computer Science, Springer, pp. 167-177, 10th International Conference on Computational Collective Intelligence, Bristol, United Kingdom, 5/09/18. https://doi.org/10.1007/978-3-319-98443-8_16

Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study. / Albishry, Nabeel; Crick, Tom; Fagade, Tesleem et al.
Proceedings of the 10th International Conference on Computational Collective Intelligence (ICCCI 2018): 5-7 September 2018 - Bristol, UK. Vol. 11055 Springer, 2018. p. 167-177 63 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Popularity and Geospatial Spread of Trends on Twitter

T2 - 10th International Conference on Computational Collective Intelligence

AU - Albishry, Nabeel

AU - Crick, Tom

AU - Fagade, Tesleem

AU - Tryfonas, Theo

PY - 2018/9

Y1 - 2018/9

N2 - 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.

AB - 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.

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KW - Topic spread

KW - Popularity

KW - Network graphs

KW - Twitter

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DO - 10.1007/978-3-319-98443-8_16

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SN - 978-3-319-98442-1

VL - 11055

T3 - Lecture Notes in Computer Science

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BT - Proceedings of the 10th International Conference on Computational Collective Intelligence (ICCCI 2018)

PB - Springer

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ER -

Albishry N, Crick T, Fagade T, Tryfonas T. Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study. In Proceedings of the 10th International Conference on Computational Collective Intelligence (ICCCI 2018): 5-7 September 2018 - Bristol, UK. Vol. 11055. Springer. 2018. p. 167-177. 63. (Lecture Notes in Computer Science). Epub 2018 Aug 8. doi: 10.1007/978-3-319-98443-8_16

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

Abstract

Publication series

Conference

Keywords

Access to Document

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Equipment

HPC (High Performance Computing) and HTC (High Throughput Computing) Facilities

Cite this