Apr 5, 2021 · Experimental results show that the WELFake model categorizes the news in real and fake with a 96.73% which improves the overall accuracy by 1.31 ...

To address this issue, this article proposes a two-phase benchmark model named WELFake based on word embedding. (WE) over linguistic features for fake news ...

This article proposes a two-phase benchmark model named WELFake based on word embedding (WE) over linguistic features for fake news detection using machine ...

According to experimental findings, the WELFake model classifies news as genuine or false with a 96.73% accuracy rate, which is better than convolutional neural ...

FAKE NEWS DETECTION: 1) ML Model Creation and Tuning: The LFS with WE is processed using six ML methods: SVM, NB, KNN, DT, Bagging, and AdaBoost. We tested ...

WELFAKE – WORD EMBEDDING OVER LINGUISTIC FEATURES FOR FAKE NEWS DETECTION. November 2022. DOI:10.46647/ijetms.2022.v06i06.080. Authors: Barath M · Barath M.

ABSTRACT - The paper introduces WELFake, a novel approach for fake news detection that leverages word embeddings over linguistic features.

By introducing integration strategies using several linguistic feature sets to categorize news articles into many domains as false or true, we extend existing ...

Experimental results show that the WELFake model categorizes the news in real and fake with a 96.73% which improves the overall accuracy by 1.31% compared to ...

Duration: 4:47
Posted: Oct 5, 2021