Matches in SemOpenAlex for { <https://semopenalex.org/work/W4293445793> ?p ?o ?g. }
Showing items 1 to 62 of
62
with 100 items per page.
- W4293445793 endingPage "778" @default.
- W4293445793 startingPage "769" @default.
- W4293445793 abstract "Social media platforms, such as Twitter, Instagram, and Facebook, have facilitated mass communication and connection. Due to the development as well as the advancement of social platforms, the spreading of fake news has increased. Many studies have been performed for detecting fake news with machine learning algorithms; but these existing methods had several difficulties, such as rapid propagation, access method and insignificant selection of features, and low accuracy of the text classification. Therefore, to overcome these issues, this paper proposed a hybrid Bidirectional Encoder Representations from Transformers — Support Vector Machine (BERT-SVM) model with a recommendation system that used to predict whether the information is fake or real. The proposed model consists of three phases: preprocessing, feature selection and classification. The dataset is gathered from Twitter social media related to COVID-19 real-time data. Preprocessing stage comprises Splitting, Stop word removal, Lemmatization and Spell correction. Term Frequency Inverse Document Frequency (TF-IDF) converter is utilized to extract the features and convert text to binary vectors. A hybrid BERT-SVM classification model is used to predict the data. Finally, the predicted data is compared with the preprocessed data. The proposed model is implemented in MATLAB software with several performance metrics carried out, and these parameters attained better performance: accuracy is 98 %, the error is 2 %, precision is 99 %, specificity is 99 %, and sensitivity is 98 %. Therefore the better effectiveness of the proposed model than existing approaches is shown. The proposed social networking analysis model provides effective fake news prediction that can be used to identify the Twitter comments, either real or fake." @default.
- W4293445793 created "2022-08-29" @default.
- W4293445793 creator A5021952761 @default.
- W4293445793 creator A5082530554 @default.
- W4293445793 date "2022-08-01" @default.
- W4293445793 modified "2023-10-18" @default.
- W4293445793 title "Light weight recommendation system for social networking analysis using a hybrid BERT-SVM classifier algorithm" @default.
- W4293445793 doi "https://doi.org/10.17586/2226-1494-2022-22-4-769-778" @default.
- W4293445793 hasPublicationYear "2022" @default.
- W4293445793 type Work @default.
- W4293445793 citedByCount "0" @default.
- W4293445793 crossrefType "journal-article" @default.
- W4293445793 hasAuthorship W4293445793A5021952761 @default.
- W4293445793 hasAuthorship W4293445793A5082530554 @default.
- W4293445793 hasBestOaLocation W42934457931 @default.
- W4293445793 hasConcept C10551718 @default.
- W4293445793 hasConcept C11413529 @default.
- W4293445793 hasConcept C119857082 @default.
- W4293445793 hasConcept C12267149 @default.
- W4293445793 hasConcept C124101348 @default.
- W4293445793 hasConcept C136764020 @default.
- W4293445793 hasConcept C148483581 @default.
- W4293445793 hasConcept C154945302 @default.
- W4293445793 hasConcept C188338183 @default.
- W4293445793 hasConcept C34736171 @default.
- W4293445793 hasConcept C41008148 @default.
- W4293445793 hasConcept C518677369 @default.
- W4293445793 hasConcept C95623464 @default.
- W4293445793 hasConceptScore W4293445793C10551718 @default.
- W4293445793 hasConceptScore W4293445793C11413529 @default.
- W4293445793 hasConceptScore W4293445793C119857082 @default.
- W4293445793 hasConceptScore W4293445793C12267149 @default.
- W4293445793 hasConceptScore W4293445793C124101348 @default.
- W4293445793 hasConceptScore W4293445793C136764020 @default.
- W4293445793 hasConceptScore W4293445793C148483581 @default.
- W4293445793 hasConceptScore W4293445793C154945302 @default.
- W4293445793 hasConceptScore W4293445793C188338183 @default.
- W4293445793 hasConceptScore W4293445793C34736171 @default.
- W4293445793 hasConceptScore W4293445793C41008148 @default.
- W4293445793 hasConceptScore W4293445793C518677369 @default.
- W4293445793 hasConceptScore W4293445793C95623464 @default.
- W4293445793 hasIssue "4" @default.
- W4293445793 hasLocation W42934457931 @default.
- W4293445793 hasLocation W42934457932 @default.
- W4293445793 hasOpenAccess W4293445793 @default.
- W4293445793 hasPrimaryLocation W42934457931 @default.
- W4293445793 hasRelatedWork W2063866587 @default.
- W4293445793 hasRelatedWork W2136124479 @default.
- W4293445793 hasRelatedWork W2296226123 @default.
- W4293445793 hasRelatedWork W2933578484 @default.
- W4293445793 hasRelatedWork W2952736244 @default.
- W4293445793 hasRelatedWork W2953978304 @default.
- W4293445793 hasRelatedWork W3092506759 @default.
- W4293445793 hasRelatedWork W4243126553 @default.
- W4293445793 hasRelatedWork W4248881655 @default.
- W4293445793 hasRelatedWork W2345184372 @default.
- W4293445793 hasVolume "22" @default.
- W4293445793 isParatext "false" @default.
- W4293445793 isRetracted "false" @default.
- W4293445793 workType "article" @default.