Journal ArticleDOI
A Link2vec-based Fake News Detection Model using Web Search Results
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TLDR
This research proposes the use of composition pattern of web links containing news content as a new source of information for fake news detection and proposes a novel embedding technique, which is called link2vec, an extension of word2vec.Abstract:
Today, the world is under siege from various kinds of fake news ranging from politics to COVID-19. Thus, many scholars have been researching automatic fake news detection based on artificial intelligence and machine learning (AI/ML) to prevent the spread of fake news. The mainstream research on detecting fake news so far has been text-based detection approaches, but they have inherent limitations such as the difficulty of short text processing and language dependency. Thus, as an alternative to the text-based approach, the context-based approach is emerging. The most common context-based approach the use of distributors’ network information in social media. However, such information is difficult to obtain, and only propagation within a single social media can be traced. Under this background, we propose the use of composition pattern of web links containing news content as a new source of information for fake news detection. To properly vectorize the composition pattern of web links, this study proposes a novel embedding technique, which is called link2vec, an extension of word2vec. To test the effectiveness and language independency of our link2vec-based model, we applied it to two real-world fake news datasets in different languages (English and Korean). As comparison models, we adopted the conventional text-based model and a hybrid model that combined text and whitelist-based link information proposed by a prior study. Results revealed that in the datasets in two languages, the link2vec-based detection models outperformed all the comparison models with statistical significance. Our research is expected to contribute to suggesting a completely new path for effective fake news detection.read more
Citations
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Journal ArticleDOI
Analyzing Machine Learning Enabled Fake News Detection Techniques for Diversified Datasets
TL;DR: In this article , a comparison of different machine learning and deep learning techniques is done to assess the performance for fake news detection using three datasets, including sentiment analysis, sentiment classification, and classification of false news.
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ARCNN framework for multimodal infodemic detection
TL;DR: In this article , the authors proposed a novel framework, the Allied Recurrent and Convolutional Neural Network (ARCNN), to detect fake news based on two different modalities: text and image.
Journal ArticleDOI
SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection
TL;DR: In this article , a novel multimodal stacked ensemble-based framework (SEMI-FND) was proposed for fake news detection, which is based on CNN and NNets.
Journal ArticleDOI
ARCNN framework for multimodal infodemic detection
Chahat Raj,Priyanka Meel +1 more
TL;DR: In this paper, the authors proposed a novel framework, the Allied Recurrent and Convolutional Neural Network (ARCNN), to detect fake news based on two different modalities: text and image.
Journal ArticleDOI
Fake news detection using parallel BERT deep neural networks
TL;DR: This article introduces MWPBert, which uses two parallel BERT networks to perform veracity detection on full-text news articles and showed that the proposed model outperformed previous models in terms of accuracy and other performance measures.
References
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Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Posted Content
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: In this paper, the Skip-gram model is used to learn high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships and improve both the quality of the vectors and the training speed.
Proceedings Article
Distributed Representations of Sentences and Documents
Quoc V. Le,Tomas Mikolov +1 more
TL;DR: Paragraph Vector is an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents, and its construction gives the algorithm the potential to overcome the weaknesses of bag-of-words models.
Posted Content
Distributed Representations of Sentences and Documents
Quoc V. Le,Tomas Mikolov +1 more
TL;DR: The authors proposed paragraph vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents, and achieved new state-of-the-art results on several text classification and sentiment analysis tasks.
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