DSS: A hybrid deep model for fake news detection using propagation tree and stance network

Davoudi, Mansour; Moosavi, Mohammad Reza; Sadreddini, Mohammad Hadi

doi:10.1016/j.eswa.2022.116635

Cited by 59 publications

(12 citation statements)

References 37 publications

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“…Finally, the effectiveness of these propagation network features in fake news detection is verified. Davoudi et al [58] used both the propagation tree and the stance network for early fake news detection. A new method for constructing the stance network has been proposed, and various graph-based features are extracted for sentiment analysis.…”

Section: Propagation-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fake News Detection on Social Networks: A Survey

Shen,

Liu,

Guo

et al. 2023

Applied Sciences

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In recent years, social networks have developed rapidly and have become the main platform for the release and dissemination of fake news. The research on fake news detection has attracted extensive attention in the field of computer science. Fake news detection technology has made many breakthroughs recently, but many challenges remain. Although there are some review papers on fake news detection, a more detailed picture for carrying out a comprehensive review is presented in this paper. The concepts related to fake news detection, including fundamental theory, feature type, detection technique and detection approach, are introduced. Specifically, through extensive investigation and complex organization, a classification method for fake news detection is proposed. The datasets of fake news detection in different fields are also compared and analyzed. In addition, the tables and pictures summarized here help researchers easily grasp the full picture of fake news detection.

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Section: Propagation-based Methodsmentioning

confidence: 99%

“…Stance network, RNN [58] FakeNewsNet Tri-relationship, TriFN [59] FakeNewsNet FANG [60] Twitter Twitter CNN, bi-SN-LSTM [67] MIB Multi-view co-attention network [68] PolitiFact, GossipCop…”

Section: Propagation Graphmentioning

confidence: 99%

Fake News Detection on Social Networks: A Survey

Shen,

Liu,

Guo

et al. 2023

Applied Sciences

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“…Davoudi et al [51] identified news articles by source, username, and URL domain. These attributes were employed as statistical characteristics in an ensemble model comprising pre-trained models, a statistical feature fusion network, a unique heuristic approach, and news article variables.…”

Section: Multimodal Fake News Detectionmentioning

confidence: 99%

EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection

Nadeem

Ahmed

et al. 2022

Sustainability

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Due to the exponential increase in internet and social media users, fake news travels rapidly, and no one is immune to its adverse effects. Various machine learning approaches have evaluated text and images to categorize false news over time, but they lack a comprehensive representation of relevant features. This paper presents an automated method for detecting fake news to counteract the spread of disinformation. The proposed multimodal EFND integrates contextual, social context, and visual data from news articles and social media to build a multimodal feature vector with a high level of information density. Using a multimodal factorized bilinear pooling, the gathered features are fused to improve their correlation and offer a more accurate shared representation. Finally, a Multilayer Perceptron is implemented over the shared representation for the classification of fake news. EFND is evaluated using a group of standard fake news datasets known as “FakeNewsNet”. EFND has outperformed the baseline and state-of-the-art machine learning and deep learning models. Furthermore, the results of ablation studies have demonstrated the efficacy of the proposed framework. For the PolitiFact and GossipCop datasets, the EFND has achieved an accuracy of 0.988% and 0.990%, respectively.

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“…Social media platforms generate a large amount of news information every day and categorize it into news in different domains [8] [9] (e.g., social, political, and military).…”

Section: Introductionmentioning

confidence: 99%

Soft-Label for Multi-Domain Fake News Detection

Wang,

Zhang,

et al. 2023

IEEE Access

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The spread of fake news across several fields has had serious negative impacts on the public and society. Existing studies have shown that the use of multi-domain labels can improve the accuracy of fake news detection models since news from different domains has different characteristics. However, the previous multi-domain strategy has a problem: if the data lacks domain labels, the domain knowledge learned by the model for each domain cannot be used to determine whether the news is true or fake. Therefore, a novel multi-domain fake news detection model (SLFEND) is proposed to address the above problems by using soft labels. First, using our proposed Leap GRU to skip useless words, the membership function module generates soft labels for each news item. Then, multi-domain features of the messages are extracted using the soft labels to obtain the final overall feature representation. Finally, the overall feature set of the news is fed into the discriminator, and a judgment is made as to whether the news is real or fake. On the Weibo21 and Thu datasets, our proposed SLFEND approach achieves F1 scores of 92.49% and 89.98% respectively, surpassing existing state-of-the-art research. Experiments on the Weibo21 dataset demonstrate the SLFEND model's practicality and efficacy. Experiments using the Thu dataset indicate that SLFEND can transfer knowledge to the news without multi-domain labels and add suitable multi-domain labels to more scientifically determine whether the news is real or fake.

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DSS: A hybrid deep model for fake news detection using propagation tree and stance network

Cited by 59 publications

References 37 publications

Fake News Detection on Social Networks: A Survey

Fake News Detection on Social Networks: A Survey

EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection

Soft-Label for Multi-Domain Fake News Detection

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