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Presentation
Analysis of Twitter trends with momentum of subjects
Ryo Manabe, Takashi Hanagaki, Shoya Yamazaki, Jun Iio (Chuo Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) This research is the analysis targeting trends formed on Twitter, with the addition of the momentum of each trending topic. In order to quantify the trend momentum, it uses the average arrival rate of tweets, including trend words. With momentum, it analyzes the process of forming a trend and identifies why some trends take a long time in the trends list from early data that trend topic has emerged. In addition, by combining the results of analyses and machine learning algorithms, this study attempts to predict the trend list.
Keyword (in Japanese) (See Japanese page) 
(in English) Twitter / Trends / Predict trends / Machine learning / / / /  
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Conference Information
Committee CW  
Conference Date 2022-12-16 - 2022-12-16 
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Paper Information
Registration To CW 
Conference Code 2022-12-CW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis of Twitter trends with momentum of subjects 
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Keyword(1) Twitter  
Keyword(2) Trends  
Keyword(3) Predict trends  
Keyword(4) Machine learning  
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1st Author's Name Ryo Manabe  
1st Author's Affiliation Chuo University (Chuo Univ.)
2nd Author's Name Takashi Hanagaki  
2nd Author's Affiliation Chuo University (Chuo Univ.)
3rd Author's Name Shoya Yamazaki  
3rd Author's Affiliation Chuo University (Chuo Univ.)
4th Author's Name Jun Iio  
4th Author's Affiliation Chuo University (Chuo Univ.)
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