Open AccessA combined deep learning model based on the ideal distance weighting method for fake news detection
Author(s) -
Sarayut Gonwirat,
Atchara Choompol,
Narong Wichapa
Publication year - 2022
Publication title -
international journal of data and network science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.296
H-Index - 7
eISSN - 2561-8156
pISSN - 2561-8148
DOI - 10.5267/j.ijdns.2022.1.003
Subject(s) - weighting , ideal (ethics) , computer science , fake news , value (mathematics) , artificial intelligence , covid-19 , data mining , machine learning , law , political science , medicine , internet privacy , disease , pathology , infectious disease (medical specialty) , radiology
Fake news has become a major problem affecting people, society, the economy and national security. This work proposes a combined deep learning model based on the ideal distance weighting method for fake news detection. The proposed model was validated on the ISOT and COVID-19 fake news datasets. Firstly, the ISOT and COVID-19 fake news datasets were collected. Secondly, the training-based models were used to provide accuracy values. After that, these values were transformed into criteria weights using the new ideal distance weighting method. Finally, the prediction value of the proposed model is calculated by the criteria weights. The results show that the proposed method is effective to distinguish the fake news datasets.