@inproceedings{75c17e2c9776442aaf4d26c912542a64,
title = "Popularity and Geospatial Spread of Trends on Twitter: A Middle Eastern Case Study",
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.",
keywords = "Trends, Topic spread, Popularity, Network graphs, Twitter",
author = "Nabeel Albishry and Tom Crick and Tesleem Fagade and Theo Tryfonas",
year = "2018",
month = sep,
doi = "10.1007/978-3-319-98443-8_16",
language = "English",
isbn = "978-3-319-98442-1",
volume = "11055",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "167--177",
booktitle = "Proceedings of the 10th International Conference on Computational Collective Intelligence (ICCCI 2018)",
note = "10th International Conference on Computational Collective Intelligence, ICCCI 2018 ; Conference date: 05-09-2018 Through 07-09-2018",
}