Feature Screening for Massive Data Analysis by Subsampling

Author(s):  
Xuening Zhu ◽  
Rui Pan ◽  
Shuyuan Wu ◽  
Hansheng Wang
2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Matteo Cinelli ◽  
Walter Quattrociocchi ◽  
Alessandro Galeazzi ◽  
Carlo Michele Valensise ◽  
Emanuele Brugnoli ◽  
...  

Abstract We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number $$R_0$$ R 0 for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors’ amplification.


2020 ◽  
Vol 79 (17-18) ◽  
pp. 12257-12288
Author(s):  
Carlos Iñiguez-Jarrín ◽  
José Ignacio Panach ◽  
Oscar Pastor López

Author(s):  
Michael Raedel ◽  
Heinz-Werner Priess ◽  
Steffen Bohm ◽  
Michael H. Walter

2016 ◽  
pp. 11-40 ◽  
Author(s):  
Murali K. Pusala ◽  
Mohsen Amini Salehi ◽  
Jayasimha R. Katukuri ◽  
Ying Xie ◽  
Vijay Raghavan
Keyword(s):  

2013 ◽  
Vol 66 (1) ◽  
pp. 539-555 ◽  
Author(s):  
Kenn Slagter ◽  
Ching-Hsien Hsu ◽  
Yeh-Ching Chung ◽  
Daqiang Zhang
Keyword(s):  

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