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A Study of Tackling Fake News with Machine Learning Approaches

AUTHOR Vp, Vidhya; Rengeswaran, Balamurugan
PUBLISHER Grin Verlag (05/01/2024)
PRODUCT TYPE Paperback (Paperback)

Description
Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely " fake news and real news", "ISOT" and "LIAR". We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset.
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Product Details
ISBN-13: 9783389024041
ISBN-10: 3389024042
Binding: Paperback or Softback (Trade Paperback (Us))
Content Language: English
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Page Count: 64
Carton Quantity: 110
Product Dimensions: 5.83 x 0.15 x 8.27 inches
Weight: 0.21 pound(s)
Country of Origin: US
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BISAC Categories
Computers | Languages - General
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Document from the year 2024 in the subject Computer Sciences - Computational linguistics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The fake news on social media and various other media is wide spreading and is a mat- ter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. Here we take three data sets namely " fake news and real news", "ISOT" and "LIAR". We try to implement six machine learning models on these data sets and trying to find their accu- racy and precision. The models we uses are Decision Tree, Random Forest, Support vector machine, Naive Bayes, KNN and LSTM. WE use tools like python scikit learn and NLP. Python scikit library can be used for feature extraction and textual analysis. We tries to find out which model works best on which data keeping the complexity of the data in mind. We would like to find a perfect model for any of the regional language. But the constrain is the availability of good dataset . So we try to propose a new dataset.
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