Impact of word embedding models on text analytics in deep learning environment: a review
The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
A scientometric analysis of deep learning approaches for detecting fake news
The unregulated proliferation of counterfeit news creation and dissemination that has been
seen in recent years poses a constant threat to democracy. Fake news articles have the …
seen in recent years poses a constant threat to democracy. Fake news articles have the …
Evaluating the effectiveness of publishers' features in fake news detection on social media
With the expansion of the Internet and attractive social media infrastructures, people prefer
to follow the news through these media. Despite the many advantages of these media in the …
to follow the news through these media. Despite the many advantages of these media in the …
Multimodal fake news detection
I Segura-Bedmar, S Alonso-Bartolome - Information, 2022 - mdpi.com
Over the last few years, there has been an unprecedented proliferation of fake news. As a
consequence, we are more susceptible to the pernicious impact that misinformation and …
consequence, we are more susceptible to the pernicious impact that misinformation and …
TextConvoNet: a convolutional neural network based architecture for text classification
This paper presents, TextConvoNet, a novel Convolutional Neural Network (CNN) based
architecture for binary and multi-class text classification problems. Most of the existing CNN …
architecture for binary and multi-class text classification problems. Most of the existing CNN …
SaTYa: trusted Bi-LSTM-Based fake news classification scheme for smart community
This article proposes a SaTya scheme that leverages a blockchain (BC)-based deep
learning (DL)-assisted classifier model that forms a trusted chronology in fake news …
learning (DL)-assisted classifier model that forms a trusted chronology in fake news …
TRIMOON: Two-Round Inconsistency-based Multi-modal fusion Network for fake news detection
Compared to ordinary news, fake news is characterized by faster dissemination and lower
production cost and therefore causes a great social harm. For these reasons, the challenge …
production cost and therefore causes a great social harm. For these reasons, the challenge …
Exploiting the Black-Litterman framework through error-correction neural networks
Abstract The Black-Litterman (BL) model is a particularly essential analytical tool for effective
portfolio management in financial services sector since it enables investment analysts to …
portfolio management in financial services sector since it enables investment analysts to …
Optnet-fake: Fake news detection in socio-cyber platforms using grasshopper optimization and deep neural network
Exposure to half-truths or lies has the potential to undermine democracies, polarize public
opinion, and promote violent extremism. Identifying the veracity of fake news is a …
opinion, and promote violent extremism. Identifying the veracity of fake news is a …
BBC-FND: An ensemble of deep learning framework for textual fake news detection
A wide spread of false news over Online Social Network platforms (OSNs) causes numerous
negative consequences. Several researchers proposed different models using machine …
negative consequences. Several researchers proposed different models using machine …