@Article{jnm.2021.019649,
AUTHOR = {Lei Guo},
TITLE = {Social Network Rumor Recognition Based on Enhanced Naive Bayes},
JOURNAL = {Journal of New Media},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {99--107},
URL = {http://www.techscience.com/JNM/v3n3/43098},
ISSN = {2579-0129},
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.},
DOI = {10.32604/jnm.2021.019649}
}