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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
Shufeng Xiong et al.
Summary: Compared to ordinary news, fake news spreads faster with lower production cost, causing significant social harm. Detecting fake news efficiently and accurately has become a research focus due to these reasons. We propose a Two-Round Inconsistency-based Multi-modal fusion Network (TRIMOON) for fake news detection, consisting of feature extraction, fusion, and classification modules. By performing two-fold inconsistency detection, we effectively filter noise generated during the fusion process. Experimental results demonstrate the superiority of our TRIMOON model over state-of-the-art approaches on Chinese and English datasets.
INFORMATION FUSION
(2023)
Article
Nursing
Alex Munyole Luvembe et al.
Summary: The proliferation of fake news is a serious issue in the online environment, and automated detection methods are required to mitigate its harmful impact. This study tackles the problem by incorporating dual emotion features, which are often overlooked in existing approaches, into fake news detection. A deep normalized attention-based mechanism is used to extract enriched dual emotion features, and an AGWu-RF classification method is proposed for accurate detection by optimizing hyper parameter values.
INTERNATIONAL JOURNAL OF ORTHOPAEDIC AND TRAUMA NURSING
(2023)
Article
Multidisciplinary Sciences
Nikolas Petrou et al.
Summary: Researchers have focused on extracting information from textual data of news articles to develop efficient fake news detection systems by exploiting linguistic characteristics common in fake news. Although these approaches have shown high performance, it has been proven that language and word use in literature are evolving. This paper aims to explore the linguistic characteristics of fake and real news over time, establishing a large dataset and introducing a novel framework for classification and feature extraction. The framework incorporates a change-point detection method to track the changes in linguistic features between fake and real news articles over time, with a focus on the importance of article titles in capturing important movements in similarity levels.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Fouzi Harrag et al.
Summary: Fake news stories have the potential to polarize society and undermine trust in the media, especially during political events. However, current NLP systems struggle to accurately interpret and classify Arabic fake news. Given the importance of determining truth in social media, research on this topic has gained significant attention. The use of state-of-the-art approaches on news content can address some challenges in detecting fake news characteristics, but auxiliary information is needed for clearer determination. Additionally, considering the "social-context-based" and "propagation-based" approaches can be helpful alternatives or complementary strategies to content-based approaches.
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Qing Liao et al.
Summary: This paper discusses the importance of fake news detection and its negative impact on society, and proposes a novel multi-task learning model (FDML model) to improve the detection performance of short fake news. Experimental results show that the model outperforms existing methods on real-world fake news dataset.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Proceedings Paper
Computer Science, Information Systems
Victor Zeng et al.
Summary: A recent survey suggests that there are no universal linguistic cues for deception, which implies the need to develop individual datasets and detectors for different types of deceptive attacks. It also means that when a new scam emerges, the entire process of data collection, annotation, and model building has to start from scratch. This paper tests this claim by creating a high-quality domain-independent deception dataset and examining whether a model can effectively detect multiple forms of deception.
CODASPY'22: PROCEEDINGS OF THE TWELVETH ACM CONFERENCE ON DATA AND APPLICATION SECURITY AND PRIVACY
(2022)
Proceedings Paper
Computer Science, Cybernetics
Ahmadreza Mosallanezhad et al.
Summary: With the rapid spread of fake news on social media, platforms face challenges in distinguishing between real and fake news. This study addresses the limitations of existing automated fake news detection models by incorporating auxiliary information such as user comments and user-news interactions into a novel reinforcement learning-based model called REAL-FND. The experiments demonstrate the effectiveness of this model, especially in scenarios with limited labeled data.
PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)
(2022)
Proceedings Paper
Computer Science, Cybernetics
Lanyu Shang et al.
Summary: This paper focuses on the critical problem of explainable multimodal COVID-19 misinformation detection and presents a generative approach to detect and explain misinformation in multimodal COVID-19 news articles. The proposed duo-generative framework explores the cross-modal association between different modalities of news content and effectively utilizes user comments for detection and explanation.
PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)
(2022)
Proceedings Paper
Computer Science, Cybernetics
Yixuan Chen et al.
Summary: Cross-modal learning is crucial for accurate fake news detection, and the proposed CAFE method effectively addresses the challenge of cross-modal ambiguity by aligning features, learning ambiguity, and capturing cross-modal correlations.
PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)
(2022)
Proceedings Paper
Computer Science, Cybernetics
Weizhi Xu et al.
Summary: This paper proposes a graph-based semantic structure mining framework for fake news detection. By modeling claims and evidences as graph-structured data and capturing the long-distance semantic dependency among dispersed relevant information through neighborhood propagation, the model successfully addresses the issues of sequential models' inability to integrate scattered information and neglect of redundant information.
PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)
(2022)
Proceedings Paper
Computer Science, Cybernetics
Xinyi Zhou et al.
Summary: This study aims to assess the intent of fake news spreaders by investigating the psychological explanations of unintentional spreading and proposing an influence graph. The results show that the assessed intent can differentiate between intentional and unintentional spreaders, and improve fake news detection techniques.
PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)
(2022)
Article
Computer Science, Information Systems
Junxiao Xue et al.
Summary: This study introduces a Multimodal Consistency Neural Network (MCNN) for detecting fake news, which consists of five subnetworks extracting features from text and image to measure and fuse the similarity of multimodal data.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
An Lao et al.
Summary: The study investigates the characteristics of rumors in terms of both linear temporal sequence and non-linear diffusion structure, proposing the RDLNP model for automatic rumor detection.
PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Xueyao Zhang et al.
Summary: Emotion is crucial in detecting fake news, focusing on both publisher emotion and social emotion, as dual emotion features can effectively enhance the performance of fake news detectors by capturing the distinctive patterns between fake and real news.
PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021)
(2021)
Proceedings Paper
Computer Science, Information Systems
Yingtong Dou et al.
Summary: This paper studies the novel problem of exploiting user preference for fake news detection and proposes a new framework, UPFD, which captures various signals from user preferences. Experimental results demonstrate the effectiveness of the proposed framework. Research indicates that users' historical and social engagements have great potentials to advance fake news detection.
SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL
(2021)
Article
Computer Science, Information Systems
Bilal Ghanem et al.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2020)
Article
Computer Science, Interdisciplinary Applications
Kai Shu et al.
Article
Computer Science, Artificial Intelligence
Ruben Tolosana et al.
INFORMATION FUSION
(2020)
Proceedings Paper
Computer Science, Cybernetics
Christine Geeng et al.
PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20)
(2020)
Proceedings Paper
Computer Science, Information Systems
Mateusz Fedoryszak et al.
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Dhruv Khattar et al.
WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019)
(2019)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Huaiwen Zhang et al.
PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19)
(2019)
Article
Computer Science, Artificial Intelligence
Christina Boididou et al.
INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Yaqing Wang et al.
KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING
(2018)
Article
Computer Science, Information Systems
Zhiwei Jin et al.
IEEE TRANSACTIONS ON MULTIMEDIA
(2017)
Proceedings Paper
Computer Science, Theory & Methods
Zhiwei Jin et al.
PROCEEDINGS OF THE 2017 ACM MULTIMEDIA CONFERENCE (MM'17)
(2017)
Proceedings Paper
Computer Science, Information Systems
Xiaomo Liu et al.
CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
(2016)
