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Article
Computer Science, Information Systems
Jing Jing et al.
Summary: This study proposes a progressive fusion network (MPFN) for multimodal fake news detection, which captures the representational information of each modality at different levels and establishes connections between modalities through feature fusion. The method achieves significant improvement in identifying fake news.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Jing Ma et al.
Summary: This paper introduces a countermeasure against rumor spreading using Generative Adversarial Networks (GAN) by generating uncertain or conflicting voices to enhance the discriminator's feature learning effectiveness. The proposed Transformer-based method improves the generated posts to closely respond to the source post and maintain the authenticity of information propagation. Extensive experiments demonstrate that our model achieves better results than state-of-the-art methods in rumor detection.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Mathematics, Interdisciplinary Applications
Linhe Zhu et al.
Summary: In this paper, a reaction-diffusion dynamic model with time delay is established to analyze the behavior of users in spreading rumors on the network. The Turing instability condition and different types of Turing patterns are studied. The effectiveness of the model is verified by numerical and Monte Carlo simulations, and the effects of different networks and time-delay conditions on pattern formation are studied. Furthermore, parameter identification and optimization control are carried out to make the model applicable to real situations.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Artificial Intelligence
Lanting Fang et al.
Summary: The wide spread of rumors on social media platforms has caused damages, raising concerns from the public and government. Detecting rumors is challenging due to the lack of reliable datasets for training classification models. To address this problem, an unsupervised model is proposed to detect rumors by measuring tweet conformity to normal patterns, with a novel tree variational autoencoder model and a cross-alignment method to discover normal tweet patterns. Experimental results on a real-world Weibo dataset show that the proposed method outperforms state-of-the-art unsupervised methods and adapts better to concept drift compared to supervised methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Fatima Haouari et al.
Summary: This research proposes techniques for verifying rumors in social media, focusing on authority finding in Twitter. They create a test collection called AuFIN consisting of rumors, authority accounts, and user accounts in Arabic Twitter. They also propose a hybrid model that combines language models, lexical, semantic, and network signals to find authorities. Their experiments show improvements in recall and precision by incorporating Twitter network features and semantic ranking. They discuss the limitations of current models and provide recommendations for future research.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Cybernetics
Pushpendu Kar et al.
Summary: Social media platforms play a significant role in real-world events and the popularity of image-based content on social media has led to concerns about the quality of content. This study proposes an automated technique for identifying fake images by characterizing the propagation patterns on social media using user and tweet features. The detection model, based on recurrent neural networks, achieves 89% accuracy in classifying fake images from real ones. The positive results indicate the potential for further development of this detection model as an automated technique for classifying images on social media.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Yucai Pang et al.
Summary: This article proposes an image recognition algorithm for detecting rumor topics, inspired by image forgery recognition techniques. The algorithm quantifies the adversarial intensity among comments using semantic information and constructs a transfer matrix of sentimental mutual influence. It also utilizes convolutional neural network for image processing. Experiments demonstrate the effectiveness of the algorithm in detecting rumor topics.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Information Systems
Saswata Roy et al.
Summary: Due to the harmful impact of fabricated information on social media, many rumor verification techniques have been introduced in recent years. However, existing techniques have limitations in accurately identifying the veracity of information. In this paper, a new deep neural network model called gDART is proposed to address this issue. gDART is able to capture various correlations in the word sequence that are often overlooked by traditional models, and it uses feature extraction and fusion components to identify deep embedded features and enhance the identification of the veracity of unverified claims. Experimental results show that gDART outperforms existing models in the task of veracity identification.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Artificial Intelligence
Chen Li et al.
Summary: This research constructs a hierarchical heterogeneous graph to associate posts containing the same high-frequency words, facilitating feature propagation, and formulates stance and rumor detection as multistage classification tasks, proposing a multigraph neural network framework. The experiments demonstrate that the method outperforms state-of-the-art in both stance and rumor detection, with better interpretability and requiring less labeled data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Zongmin Li et al.
Summary: This paper studies the lifecycle of social media rumor refutation effectiveness, including lifespan, peak value, and distribution. It provides decision-making recommendations to enhance the persistence and intensity of rumor refutation effectiveness.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Editorial Material
Multidisciplinary Sciences
Jonathan Osborne et al.
Proceedings Paper
Computer Science, Cybernetics
Tiening Sun et al.
Summary: This article proposes a novel approach called Graph Adversarial Contrastive Learning (GACL) to detect and combat internet rumors. The method utilizes the structural information of rumor propagation trees and employs contrastive learning and adversarial feature transformation to improve the model's robustness and accuracy.
PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)
(2022)
Proceedings Paper
Computer Science, Information Systems
Ruichao Yang et al.
Summary: The propagation tree structure of rumors on social media can provide valuable clues, while rumor verification and stance detection are two related tasks that can support each other. However, stance detection typically requires a large amount of labeled data, which is difficult to obtain in practice.
PROCEEDINGS OF THE 45TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '22)
(2022)
Article
Computer Science, Artificial Intelligence
Lingwei Wei et al.
Summary: This article proposes a novel neuro-fuzzy method, fuzzy graph convolutional networks (FGCNs), for rumor detection. By designing a new graph construction strategy and incorporating edge fuzzification modules, this method can sufficiently understand uncertain interactions in information cascades and capture more discriminative and robust structural features.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Turker Tuncer et al.
Summary: An innovative method for speech emotion recognition is proposed in this study, utilizing cryptographic structure and iterative neighborhood component analysis to achieve high classification performance. Experimental results demonstrate that the method attained high classification accuracies on multiple public databases, with potential for application in large-scale databases and healthcare settings.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Multidisciplinary Sciences
Gordon Pennycook et al.
Summary: In recent years, there has been growing concern over the spread of false information on social media. Research has shown that while most people value sharing accurate news, the veracity of headlines does not necessarily impact their intentions to share. By shifting attention to accuracy, the quality of news that people share can be improved, challenging the belief that people prioritize partisanship over accuracy.
Article
Computer Science, Artificial Intelligence
Changhe Song et al.
Summary: Rumors spread quickly on online social media, and there is a need for efficient methods to automatically detect rumors. This paper proposes a novel early rumor detection model, Credible Early Detection (CED), which significantly reduces the time span for prediction with better accuracy performance than existing methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Information Systems
Shiwen Ni et al.
Summary: Fake news on social media is a widespread and serious problem. The study developed a novel neural network model, MVAN, which significantly outperforms existing methods in detecting fake news, capturing information from source tweets and propagation structure to provide explanations.
Article
Computer Science, Information Systems
Sarah A. Alkhodair et al.
INFORMATION PROCESSING & MANAGEMENT
(2020)
Article
Computer Science, Interdisciplinary Applications
Kai Shu et al.
Article
Computer Science, Artificial Intelligence
Jing Ma et al.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2020)
Proceedings Paper
Computer Science, Cybernetics
Liang Ke et al.
2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
(2020)
Article
Acoustics
Qiuqiang Kong et al.
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
(2020)
Article
Business
Victor P. Seidel et al.
ACADEMY OF MANAGEMENT REVIEW
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Yixuan Chen et al.
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19)
(2019)
Proceedings Paper
Computer Science, Information Systems
Han Guo et al.
CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
(2018)
Article
Political Science
Adam J. Berinsky
BRITISH JOURNAL OF POLITICAL SCIENCE
(2017)
