A New Topic Modeling Method for Tweets Comparison

被引：0

作者：

Bezerra, Jose Fabio Ribeiro ^{[1
]}

Pietranik, Marcin ^{[1
]}

Thanh Thuy Nguyen ^{[2
]}

Kozierkiewicz, Adrianna ^{[1
]}

机构：

[1] Wroclaw Univ Sci & Technol, Fac Informat & Commun Technol, Dept Appl Informat, Wyb Wyspianskiego 27, PL-50370 Wroclaw, Poland

[2] Vietnam Natl Univ, Univ Engn & Technol, Hanoi, Vietnam

来源：

COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2023 | 2023年 / 14162卷

关键词：

topic modeling; fake news detection; tweet comparison; news;

D O I：

10.1007/978-3-031-41456-5_25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fake news detection is a real problem, especially in social media. The untruth news dispreads very quickly and brings huge damage. This paper is devoted to proposing a topic modeling method that allows for comparing tweets, which is one of the stages of fake news detection. Our method, named Content Weighted Topic (CWT), is based on applyingWordNet. Experiments showed that our method is better than the well-known Latent Dirichlet Allocation (LDA) algorithm regarding topic coherence measure. Our CWT method assigns topics for tweets that are more consistent than topics assigned by the LDA algorithm.

引用

页码：326 / 336

页数：11

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