Title of article
A Deep Model on Hoax Detection Using Feed Forward Neural Network and LSTM
Author/Authors
kumar, guntha venkata dhanush ramaiah institute of technology, Bangalore, India , jadhav, mamatha v ramaiah institute of technology, Bangalore, India , tadisetti, anvesh ramaiah institute of technology, Bangalore, India , kiran, . ramaiah institute of technology, Bangalore, India
From page
562
To page
662
Abstract
The topic of hoax news detection on social media has recently pulled in enormous consideration. Social media not taking any credibility for the news being spread in it makes it more difficult to contain the hoax news. The essential counter measure of comparing websites against a list of labeled hoax news sources is inflexible, and so a machine learning approach is desirable. Our project aims to use Neural Networks to detect hoax news directly, based on the text content of news articles. The model concentrates on discovering hoax news origins, based on the many articles originating from it. When a source is spotted as a maker of hoax news, we can predict with high reliability that other articles from that will similarly be hoax news. Focusing on sources augments our article mis categorization resilience, since we at that point have various facts focuses originating from each source.
Keywords
Neural Networks , LSTM , FFNN , RNN , Tensor Flow , Keras
Journal title
Webology
Journal title
Webology
Record number
2750695
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