Exploiting discourse structure of traditional digital media to enhance automatic fake news detection

Bonet-Jover, Alba; Piad-Morffis, Alejandro; Saquete, Estela; Martínez-Barco, Patricio; García-Cumbreras, Miguel Á.

doi:10.1016/j.eswa.2020.114340

Cited by 22 publications

(20 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Apuke and Omar [32] studied Covid19 and fake news over social media users using a gratification framework extended by 'altruism' motivation. Working over traditional digital journalism where long articles of text are considered, Bonet-J et al [33] proposed 5W1H based model, which is essential in lead construction. 5W's are What, Who, Where, When, Why, and How.…”

Section: Single Modularity Approachmentioning

confidence: 99%

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

2023

KSII TIIS

View full text Add to dashboard Cite

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNet-B0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

show abstract

Section: Single Modularity Approachmentioning

confidence: 99%

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

2023

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Intentionally deceptive stories contain more references to sources; however, those sources are rarely presented in a way that makes them traceable. Identifiable names are not provided, as sources are typically anonymous and vague (Bonet-Jover et al 2021). Others refer to the use of conspiratorial and dubious sources, without fact-checking (Bradshaw et al 2020;Marchal et al 2019;Neudert, Howard, and Kollanyi 2019).…”

Section: Verifiabilitymentioning

confidence: 99%

What Does Fake Look Like? A Review of the Literature on Intentional Deception in the News and on Social Media

et al. 2021

View full text Add to dashboard Cite

This paper focuses on the content features of intentional deceptive information in the news (i.e., fake news) and on social media. Based on an extensive review of relevant literature (i.e., political journalism and communication, computational linguistics), we take stock of existing knowledge and present an overview of the structural characteristics that are indicative of intentionally deceptive information. We discuss the strength of underlying empirical evidence and identify underdeveloped areas of research. With this paper, we aim to contribute to the systematic study of intentional deception in the news and on social media and to help setting up new lines of research in which intentionally deceptive news items can be operationalized in consistent ways.

show abstract

“…The total size of the dataset was 5182 fact-checked news articles. Authors in paper [ 54 ] presented a benchmark Spanish fake news dataset. They created the dataset for health news.…”

Section: Related Workmentioning

confidence: 99%

IFND: a benchmark dataset for fake news detection

Sharma

Garg

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Spotting fake news is a critical problem nowadays. Social media are responsible for propagating fake news. Fake news propagated over digital platforms generates confusion as well as induce biased perspectives in people. Detection of misinformation over the digital platform is essential to mitigate its adverse impact. Many approaches have been implemented in recent years. Despite the productive work, fake news identification poses many challenges due to the lack of a comprehensive publicly available benchmark dataset. There is no large-scale dataset that consists of Indian news only. So, this paper presents IFND (Indian fake news dataset) dataset. The dataset consists of both text and images. The majority of the content in the dataset is about events from the year 2013 to the year 2021. Dataset content is scrapped using the Parsehub tool. To increase the size of the fake news in the dataset, an intelligent augmentation algorithm is used. An intelligent augmentation algorithm generates meaningful fake news statements. The latent Dirichlet allocation (LDA) technique is employed for topic modelling to assign the categories to news statements. Various machine learning and deep-learning classifiers are implemented on text and image modality to observe the proposed IFND dataset's performance. A multi-modal approach is also proposed, which considers both textual and visual features for fake news detection. The proposed IFND dataset achieved satisfactory results. This study affirms that the accessibility of such a huge dataset can actuate research in this laborious exploration issue and lead to better prediction models.

show abstract

Exploiting discourse structure of traditional digital media to enhance automatic fake news detection

Cited by 22 publications

References 43 publications

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

What Does Fake Look Like? A Review of the Literature on Intentional Deception in the News and on Social Media

IFND: a benchmark dataset for fake news detection

Contact Info

Product

Resources

About