Memory-Guided Multi-View Multi-Domain Fake News Detection

Zhu, Yongchun; Sheng, Qiang; Cao, Juan; Nan, Qiong; Shu, Kai; Wu, Minghui; Wang, Jindong; Zhuang, Fuzhen

doi:10.1109/tkde.2022.3185151

Cited by 41 publications

(15 citation statements)

References 66 publications

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“…As news articles are mainly text, content-based methods start by manually extracting linguistic features and predicting fake news using common classifiers such as SVM [4]. Such linguistic features have been related to lexicons (e.g., bag-ofwords) [5], POSs [5], [6], context-free grammars (production rules) [4], [5], RRs [5], [8], readability [6], [18], and ngrams that preserve the sequences of words or POSs [7]. Though news features can be easily interpreted within this machine learning framework, features cannot be automatically extracted, which can significantly impact the prediction performance; hence, the performance heavily relies on experts' involvement and experience.…”

Section: Related Workmentioning

confidence: 99%

Linguistic-style-aware Neural Networks for Fake News Detection

Zhou¹,

Li²,

Li³

et al. 2023

Preprint

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We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal. We first generate the hierarchical linguistic tree of news documents; by doing so, we translate each news document's linguistic style into its writer's usage of words and how these words are recursively structured as phrases, sentences, paragraphs, and, ultimately, the document. By integrating the hierarchical linguistic tree with the neural network, the proposed method learns and classifies the representation of news documents by capturing their locally sequential and globally recursive structures that are linguistically meaningful. It is the first work offering the hierarchical linguistic tree and the neural network preserving the tree information to our best knowledge. Experimental results based on public real-world datasets demonstrate the proposed method's effectiveness, which can outperform state-of-the-art techniques in classifying short and long news documents. We also examine the differential linguistic style of fake news and the truth and observe some patterns of fake news. 1

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Section: Related Workmentioning

confidence: 99%

Linguistic-style-aware Neural Networks for Fake News Detection

Zhou¹,

Li²,

Li³

et al. 2023

Preprint

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“…6 use pre-trained language models in multi-modal framework. Besides, multi-domain methods 7,8 focus on specific scenarios.…”

Section: Related Workmentioning

confidence: 99%

News environment-knowledge perception for fake news detection

Jia

Lin

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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Automatic fake news detection is crucial for society. Existing methods mainly focus on the post's content or taking advantage of external sources to make a decision. Recently a new approach called NEP has been proposed, it constructed out news environment for each news to capture popularity and novelty as other evidence. However, NEP neglects the changeable environment and it is weak when news breaks out or is published several times. Commonsense knowledge related to a post has sufficient reliability compared with the news environment or external news evidence, and it can bring a stable benefit for distinguishing fake news. Based on this, we search out commonsense knowledge for each post and propose the News Environment-Knowledge Perception (NEKP) based on NEP. For each post, we search out the related knowledge items on Wikimedia. Then we fuse this knowledge through an existing fake news detector-DeClarE.Finally, we fuse the news environment, knowledge, and the news itself to make a detection. Experiments on NEP datasets show that commonsense knowledge is another helpful piece of evidence.

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“…However, we recognize that these prior biases primarily originate not only from key entities in news articles but also from significant contextual indicators such as emotional words like "shocks" in Figure 1(b). Since fake news often exhibits distinctive writing styles [59], characterized by exaggeration or extreme stances, it becomes imperative to adaptively learn and mitigate biases towards specific words rather than focusing on entity words.…”

Section: Introductionmentioning

confidence: 99%

MSynFD: Multi-hop Syntax Aware Fake News Detection

Xiao,

Zhang,

Shi

et al. 2024

Proceedings of the ACM Web Conference 2024

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The proliferation of social media platforms has fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contextual information to enhance the detection of fake news by analyzing news content and/or its social context. However, these methods often overlook essential textual news content (articles) and heavily rely on sequential modeling and global attention to extract semantic information. These existing methods fail to handle the complex, subtle twists 1 in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing. To bridge these significant gaps, we propose a novel multi-hop syntax aware fake news detection (MSynFD) method, which incorporates complementary syntax information to deal with subtle twists in fake news. Specifically, we introduce a syntactical dependency graph and design a multi-hop subgraph aggregation mechanism to capture multi-hop syntax. It extends the effect of word perception, leading to effective noise filtering and adjacent relation enhancement. Subsequently, a sequential relative position-aware Transformer is designed to capture the sequential information, together with an elaborate keyword debiasing module to mitigate the prior bias. Extensive experimental results on two public benchmark datasets verify the effectiveness and superior performance of our proposed MSynFD over state-of-the-art detection models. CCS CONCEPTS• Computing methodologies → Artificial intelligence.

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Memory-Guided Multi-View Multi-Domain Fake News Detection

Cited by 41 publications

References 66 publications

Linguistic-style-aware Neural Networks for Fake News Detection

Linguistic-style-aware Neural Networks for Fake News Detection

News environment-knowledge perception for fake news detection

MSynFD: Multi-hop Syntax Aware Fake News Detection

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