@inproceedings{792cf72906434acc8dd74b38d9c5f5ea,
title = "Massive meme identification and popularity analysis in geopolitics",
abstract = "Geopolitics is a long-lasting key issue for governments and nations to assess the international political landscape. The great proliferation of social media in recently years have provided a new avenue to make such political actions in a data driven manner. As the information consumption ability of human is limited, there demands an automatic approach to effectively identify and trace the bursts continuously emerging on social media platforms. Existing studies focusing on named entities recognition or topic detection could provide useful insights for analyzing events that are already known, yet they are incapable of identifying timely emerging trending catchphrase or topics, or memes in general.To tackle with this issue, we elaborate a framework to identify online memes and trace their future dynamics. This framework identify memes based on their independency with regard to the context, and aggregate literal variants of a same meme together into a memeplex with a newly proposed MemeMesh algorithm. Evaluation results on a large scale Twitter dataset suggest that the framework could identify geopolitical memes effectively. Further exploration on meme popularity factors reveals that popularity memes tend to generate more variants during their diffusion, and establish their dominance by attracting a large volume of active users engaging in their diffusion. Causality analysis between meme diversity and user volume suggests that high diversity of meme variants can attract more users involved in spreading a meme at the initial, but these users seldom regenerate more variants in the later time.",
keywords = "Meme identification, MemeMesh, Popularity factors",
author = "Saike He and Hongtao Yang and Xiaolong Zheng and Bo Wang and Yujun Zhou and Yanjun Xiong and Daniel Zeng",
note = "Funding Information: This work was supported in part by the following grants: the National key R&D program of China under Grant Nos. 2017YFC1200302, 2016QY02D0305, 2017YFC0820105, the National Natural Science Foundation of China under Grant Nos. 71602184, 71472175, 71603253, 71621002, the Ministry of Health of China under Grant No. 2017ZX10303401-002, the R and D Program of Beijing Municipal Education Commission under Grant No. KM201910853001, and the Key Program of Beijing Polytechnic College under Grant No. bgzyky 201734z. Funding Information: This work was supported in part by the following grants: the National key R&D program of China under Grant Nos. 2017YFC1200302, 2016QY02D0305, 2017YFC0820105, the National Natural Science Foundation of China under Grant Nos. 71602184, 71472175, 71603253, 71621002, the Ministry of Health of China under Grant No. 2017ZX10303401-002, the R&D Program of Beijing Municipal Education Commission under Grant No. KM201910853001, and the Key Program of Beijing Polytechnic College under Grant No. bgzyky 201734z.; 17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019 ; Conference date: 01-07-2019 Through 03-07-2019",
year = "2019",
month = jul,
doi = "10.1109/ISI.2019.8823294",
language = "English (US)",
series = "2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "116--121",
editor = "Xiaolong Zheng and Ahmed Abbasi and Michael Chau and Alan Wang and Lina Zhou",
booktitle = "2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019",
}