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Improving fake news classification using dependency grammar
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 9, p e0256940 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.
- Subjects :
- Viral Diseases
Computer science
Datasets as Topic
Social Sciences
computer.software_genre
Trees
Machine Learning
Medical Conditions
Dependency grammar
Medicine and Health Sciences
Data Mining
media_common
Grammar
Multidisciplinary
Parsing
Applied Mathematics
Simulation and Modeling
Eukaryota
Plants
Semantics
Infectious Diseases
Physical Sciences
Medicine
Algorithms
Natural language processing
Sentence
Research Article
Linguistic Morphology
Computer and Information Sciences
Deception
Science
media_common.quotation_subject
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
Decision tree
Research and Analysis Methods
Machine Learning Algorithms
Artificial Intelligence
Humans
Syntax
tf–idf
business.industry
Organisms
Biology and Life Sciences
Linguistics
Covid 19
Artificial intelligence
business
Social Media
computer
Mathematics
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....44b9d0b3dcdb89a4851dc362ca5bc844