FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210581970> ?p ?o ?g. }

• W4210581970 endingPage "334" @default.
• W4210581970 startingPage "317" @default.
• W4210581970 abstract "AbstractFake news is any content or information that is false and often generated to mislead its readers in believing something which is not true. Fake news has become one of major threats that can harm someone’s reputation. It often circulates wrong or made up information about various products, events, people or entity. The deliberate making of such news is escalating drastically these days. Fake news deceives us in taking wrong decisions. Therefore, Fake News Detection has attained immense deal of interest from researchers all over the world. In this chapter, a machine learning approach has been proposed named FakeTouch starting with Natural Language Processing based concept by applying text processing, cleaning and extraction techniques. This approach aim to arrange the information to be “obeyed” into each classification model for training and tuning parameters for every model to bring out the optimized and best prediction to find out the Fake news. To evaluate the proposed framework, three use cases with three different datasets has been developed during this study. The proposed framework will also help to understand what amount of data is responsible for detecting fake news, trying to stage the linguistic differences between fake and true articles providing a visualization of the results using different visualization tools. This chapter also presents a comprehensive performance evaluation to compare different well known machine learning classifiers like Support Vector Machine, Naïve Bayes Method, Decision Tree Classifier, Random Forest, Logistic Regression as well as to develop an ensemble method (Bagging & Boosting) like XGBClassifier, Bagging Classifier of different combinations of classification models to identify which will give the best optimal results for three part of datasets. As a result, it has been found that with an appropriate set of features extracted from the texts and the headlines, XGB classifier can effectively classify fake news with very high detection rate. This framework also provides a strong baseline of an intelligent anti-fake news detector.KeywordsFake news detectionScrapingSocial mediaText classificationComparison of algorithmsMachine learningNatural language processing" @default.
• W4210581970 created "2022-02-08" @default.
• W4210581970 creator A5018409565 @default.
• W4210581970 creator A5019961343 @default.
• W4210581970 creator A5061289083 @default.
• W4210581970 creator A5064701742 @default.
• W4210581970 creator A5065391454 @default.
• W4210581970 creator A5077215268 @default.
• W4210581970 date "2022-01-01" @default.
• W4210581970 modified "2023-10-16" @default.
• W4210581970 title "FakeTouch: Machine Learning Based Framework for Detecting Fake News" @default.
• W4210581970 cites W2564112458 @default.
• W4210581970 cites W2593408211 @default.
• W4210581970 cites W2729736251 @default.
• W4210581970 cites W2769470793 @default.
• W4210581970 cites W2791544114 @default.
• W4210581970 cites W2810026022 @default.
• W4210581970 cites W2884148093 @default.
• W4210581970 cites W2901432156 @default.
• W4210581970 cites W2902600998 @default.
• W4210581970 cites W2906971970 @default.
• W4210581970 cites W2912642460 @default.
• W4210581970 cites W2925285378 @default.
• W4210581970 cites W2951307134 @default.
• W4210581970 cites W2962830212 @default.
• W4210581970 cites W2963416784 @default.
• W4210581970 cites W2964243798 @default.
• W4210581970 cites W2980871247 @default.
• W4210581970 cites W2998948844 @default.
• W4210581970 cites W3000155280 @default.
• W4210581970 cites W3001895040 @default.
• W4210581970 cites W3046137360 @default.
• W4210581970 cites W3093031973 @default.
• W4210581970 cites W3152519896 @default.
• W4210581970 cites W3164409774 @default.
• W4210581970 cites W4234488844 @default.
• W4210581970 cites W4239097630 @default.
• W4210581970 cites W4288079542 @default.
• W4210581970 doi "https://doi.org/10.1007/978-3-030-87954-9_15" @default.
• W4210581970 hasPublicationYear "2022" @default.
• W4210581970 type Work @default.
• W4210581970 citedByCount "0" @default.
• W4210581970 crossrefType "book-chapter" @default.
• W4210581970 hasAuthorship W4210581970A5018409565 @default.
• W4210581970 hasAuthorship W4210581970A5019961343 @default.
• W4210581970 hasAuthorship W4210581970A5061289083 @default.
• W4210581970 hasAuthorship W4210581970A5064701742 @default.
• W4210581970 hasAuthorship W4210581970A5065391454 @default.
• W4210581970 hasAuthorship W4210581970A5077215268 @default.
• W4210581970 hasConcept C108827166 @default.
• W4210581970 hasConcept C119857082 @default.
• W4210581970 hasConcept C12267149 @default.
• W4210581970 hasConcept C13672336 @default.
• W4210581970 hasConcept C144024400 @default.
• W4210581970 hasConcept C154945302 @default.
• W4210581970 hasConcept C169258074 @default.
• W4210581970 hasConcept C17744445 @default.
• W4210581970 hasConcept C199539241 @default.
• W4210581970 hasConcept C204321447 @default.
• W4210581970 hasConcept C2777363581 @default.
• W4210581970 hasConcept C2779756789 @default.
• W4210581970 hasConcept C36289849 @default.
• W4210581970 hasConcept C41008148 @default.
• W4210581970 hasConcept C46686674 @default.
• W4210581970 hasConcept C48798503 @default.
• W4210581970 hasConcept C52001869 @default.
• W4210581970 hasConcept C84525736 @default.
• W4210581970 hasConcept C95623464 @default.
• W4210581970 hasConceptScore W4210581970C108827166 @default.
• W4210581970 hasConceptScore W4210581970C119857082 @default.
• W4210581970 hasConceptScore W4210581970C12267149 @default.
• W4210581970 hasConceptScore W4210581970C13672336 @default.
• W4210581970 hasConceptScore W4210581970C144024400 @default.
• W4210581970 hasConceptScore W4210581970C154945302 @default.
• W4210581970 hasConceptScore W4210581970C169258074 @default.
• W4210581970 hasConceptScore W4210581970C17744445 @default.
• W4210581970 hasConceptScore W4210581970C199539241 @default.
• W4210581970 hasConceptScore W4210581970C204321447 @default.
• W4210581970 hasConceptScore W4210581970C2777363581 @default.
• W4210581970 hasConceptScore W4210581970C2779756789 @default.
• W4210581970 hasConceptScore W4210581970C36289849 @default.
• W4210581970 hasConceptScore W4210581970C41008148 @default.
• W4210581970 hasConceptScore W4210581970C46686674 @default.
• W4210581970 hasConceptScore W4210581970C48798503 @default.
• W4210581970 hasConceptScore W4210581970C52001869 @default.
• W4210581970 hasConceptScore W4210581970C84525736 @default.
• W4210581970 hasConceptScore W4210581970C95623464 @default.
• W4210581970 hasLocation W42105819701 @default.
• W4210581970 hasOpenAccess W4210581970 @default.
• W4210581970 hasPrimaryLocation W42105819701 @default.
• W4210581970 hasRelatedWork W3127425528 @default.
• W4210581970 hasRelatedWork W3143658565 @default.
• W4210581970 hasRelatedWork W3204641204 @default.
• W4210581970 hasRelatedWork W3212730154 @default.
• W4210581970 hasRelatedWork W4200057378 @default.
• W4210581970 hasRelatedWork W4205958290 @default.
• W4210581970 hasRelatedWork W4249229055 @default.
• W4210581970 hasRelatedWork W4283016678 @default.

• next