Open Access

ARTICLE

Social Network Rumor Recognition Based on Enhanced Naive Bayes

Lei Guo^*

School of Computer & Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Lei Guo. Email:

Journal of New Media 2021, 3(3), 99-107. https://doi.org/10.32604/jnm.2021.019649

Received 22 April 2021; Accepted 29 June 2021; Issue published 13 July 2021

Abstract

In recent years, with the increasing popularity of social networks, rumors have become more common. At present, the solution to rumors in social networks is mainly through media censorship and manual reporting, but this method requires a lot of manpower and material resources, and the cost is relatively high. Therefore, research on the characteristics of rumors and automatic identification and classification of network message text is of great significance. This paper uses the Naive Bayes algorithm combined with Laplacian smoothing to identify rumors in social network texts. The first is to segment the text and remove the stop words after the word segmentation is completed. Because of the datasensitive nature of Naive Bayes, this paper performs text preprocessing on the input data. Then a naive Bayes classifier is constructed, and the Laplacian smoothing method is introduced to solve the problem of using the naive Bayes model to estimate the zero probability in rumor recognition. Finally, experiments show that the Naive Bayes algorithm combined with Laplace smoothing can effectively improve the accuracy of rumor recognition.

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Keywords

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