Open AccessFake News Detection in Machine Learning Hybrid Model
Author(s) -
P Chandana,
K.Sree Vijaya Lakshmi
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a3067.059120
Subject(s) - fake news , voting , categorization , computer science , perspective (graphical) , recurrent neural network , artificial intelligence , public opinion , machine learning , deep learning , decision tree , politics , plan (archaeology) , random forest , artificial neural network , data science , political science , internet privacy , history , law , archaeology
Now a day's prediction of fake news is somewhat an important aspect. The spreading of fake news mainly misleads the people and some false news that led to the absence of truth and stirs up the public opinion. It might influence some people in the society which leads to a loss in all directions like financial, psychological and also political issues, affecting voting decisions during elections etc. Our research work is to find reliable and accurate model that categorize a given news in dataset as fake or real. The existing techniques involved in are from a deep learning perspective by Recurrent Neural Network (RNN) technique models Vanilla, Gated Recurrent Unit (GRU) and Long Short-Term Memories (LSTMs) by applying on LAIR dataset. So we come up with a different plan to increase the accuracy by hybridizing Decision Tree and Random Forest.