Applying Machine Learning to Detect Fake News
DOI:
https://doi.org/10.17010/ijcs/2019/v4/i1/142411Keywords:
Count Vectorizer
, Fake News Detection, Machine Learning, NLP, Text Analytics, TFIDF Vectorizer.Manuscript Received
, October 20, 2018, Revised, November 2, Accepted, November 27, 2018. Date of Publication, January 6, 2019.Abstract
Spread of fake news has become a big problem in recent times and this has become a source of discomfort in the society in many instances. Application of sophisticated Machine Learning algorithms for detection of fake news has shown some success and it is a new area which shows immense opportunities for practical use and further research. The detection of fake news using machine learning algorithms have been discussed in details in this paper. News items are unstructured data and this paper shows how unstructured news items can be turned into structured form using Count vectorization and TFIDF vectorization techniques. This paper also shows how machine learning models can be developed and the classification can be done using the newly structured data employing sophisticated developed Machine Learning algorithms.Downloads
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