FAKE NEWS IDENTIFICATION AND CLASSIFICATION USING MACHINE LEARNING

Abstract

A lot of information comes through the social media and people get 70 percent of their news through the social media. It is however also a nest of wickedness that propagates disbelieves and creates fakes. The paper highlights the semantically based identification of a false news to explore and understand the depth of misinformation and draw semantic knowledge to make dynamic decisions. The fake news recognition system targets to formulate an ontology to recognize hypothesis that is employed to swindle social media users by means of logical inference. The given model implies dividing the news content into the fictitious categories and semantically analyzing the news content of the data set. FNIOnt results are projected to three of ML based classifiers to classify the false news: Random Forest (RF) classifiers, Logistic Regression (LR) classifiers, and long short-term memory (LSTM) classifiers. The suggested method is superior to the previous fake news methods, and its identification and accuracy rate is 99 percent. The above findings confirm that machine learning models are better than previous models after the semantic feature investigation on new data sets. The other challenge, which is vital in the task of detecting fake news, is the high rate of adaptation of various strategies by the people behind the identity of fake or misleading news. Since machine learning systems are improving their performance in identifying fake news, maskers of fake stories are constantly changing their methods, either discovering new methods of avoiding detection or changing their writing styles. As an example, they can resort to less direct methods of manipulation like the use of half-truths or statements that are hard to argue with, thereby making the identification more complicated. As a reaction, the machine learning models will have to be made dynamic to respond to these novel methods and can enhance over time by analyzing new data and leaning to the new trends in the generation of fake news. Fake news detection is one of the areas where deep learning, a branch of machine learning using learning with multiple layers that take the form of artificial neural networks, was shown to have potential. Neural networks such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers can automatically infer complex information in raw text and so the manual selection of features is unnecessary. These models are very applicable in the processing of unstructured text data because they grasp semantic and syntactic relationship between words as opposed to machine learning models, which are limited to understanding such relationships. To illustrate, deep learning models can be trained to approach context and sentiment of a news article to allow them to differentiate between real and fake content even under the conditions of minor manipulation.