Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating investigative journalism are often inefficient and struggle with breaking news. Recent advances in large language models (LLMs) enable leveraging externally retrieved reports as evidence for detection and explanation generation, but unverified reports may introduce inaccuracies. Moreover, effective explainable fake news detection should provide a comprehensible explanation for all aspects of a claim to assist the public in verifying its accuracy. To address these challenges, we propose a graph-enhanced defense framework (G-Defense) that provides fine-grained explanations based solely on unverified reports. Specifically, we construct a claim-centered graph by decomposing the news claim into several sub-claims and modeling their dependency relationships. For each sub-claim, we use the retrieval-augmented generation (RAG) technique to retrieve salient evidence and generate competing explanations. We then introduce a defense-like inference module based on the graph to assess the overall veracity. Finally, we prompt an LLM to generate an intuitive explanation graph. Experimental results demonstrate that G-Defense achieves state-of-the-art performance in both veracity detection and the quality of its explanations.

This study proposes the concept of an adjacent two-end or multi-end link of a software-defined network to support the transmission of data from electroencephalogram as well as audio and video streaming through spread spectrum communications. Instead of solving the problem of the software-defined scheme of prioritization in networking-related calculation and communication, we identify the southbound–northbound structure of current neural or informational networks in control and perceptual interactions. The proposed framework of multi-lead and multi-media structures allows the human-centric metaverse to execute different operations for local and global planning schemes based on the transmission of high-speed lead or media-related data through spread spectrum streaming. This depends on the kind of brain–computer interaction and is similar to the system of multi-modal perception in routing or switching. First, we design the field-programmable gate array for conventional routing nodes used in computation by different neural networks. Second, we demonstrate its enhanced applicability with a software core for multiple routers in the computational power network of the Internet of Brains in different brain–computer smart terminals. The results of simulations verified the benefits of the proposed model and structure and revealed a variety of important operational indicators.

Most fake news detection methods learn latent feature representations based on neural networks, which makes them black boxes to classify a piece of news without giving any justification. Existing explainable systems generate veracity justifications from investigative journalism, which suffer from debunking delayed and low efficiency. Recent studies simply assume that the justification is equivalent to the majority opinions expressed in the wisdom of crowds. However, the opinions typically contain some inaccurate or biased information since the wisdom of crowds is uncensored. To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework. Specifically, we first propose an evidence extraction module to split the wisdom of crowds into two competing parties and respectively detect salient evidences. To gain concise insights from evidences, we then design a prompt-based module that utilizes a large language model to generate justifications by inferring reasons towards two possible veracities. Finally, we propose a defense-based inference module to determine veracity via modeling the defense among these justifications. Extensive experiments conducted on two real-world benchmarks demonstrate that our proposed method outperforms state-of-the-art baselines in terms of fake news detection and provides high-quality justifications.

In the era of information explosion, named entity recognition (NER) has attracted widespread attention in the field of natural language processing, as it is fundamental to information extraction. Recently, methods of NER based on representation learning, e.g., character embedding and word embedding, have demonstrated promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information, e.g., capitalization, inter-word relations, keywords, and lexical phrases, from multilevel perspectives. Intuitively, multilevel features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel attentive multilevel feature fusion (AMFF) model for NER, which captures the multilevel features in the current context from various perspectives. It consists of four components to, respectively, capture the local character-level (CL), global character-level (CG), local word-level (WL), and global word-level (WG) features in the current context. In addition, we further define document-level features crafted from other sentences to enhance the representation learning of the current context. To this end, we introduce a novel context-aware attentive multilevel feature fusion (CAMFF) model based on AMFF, to fully leverage document-level features from all the previous inputs. The obtained multilevel features are then fused and fed into a bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) network for the final sequence labeling. Extensive experiments on four benchmark datasets demonstrate that our proposed AMFF and CAMFF models outperform a set of state-of-the-art baseline methods and the features learned from multiple levels are complementary.

The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space. Does there exist better policies outside of this space? By using a novel surrogate objective that employs the sigmoid function (which provides an interesting way of exploration), we found that the answer is "YES", and the better policies are in fact located very far from the clipped space. We show that PPO is insufficient in "off-policyness", according to an off-policy metric called DEON. Our algorithm explores in a much larger policy space than PPO, and it maximizes the Conservative Policy Iteration (CPI) objective better than PPO during training. To the best of our knowledge, all current PPO methods have the clipping operation and optimize in the clipped policy space. Our method is the first of this kind, which advances the understanding of CPI optimization and policy gradient methods. Code is available at https://github.com/raincchio/P3O.

In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public.Rumors can trigger versatile, mostly controversial stance expressions among social media users.Rumor verification and stance detection are different yet relevant tasks.Fake news debunking primarily focuses on determining the truthfulness of news articles, which oversimplifies the issue as fake news often combines elements of both truth and falsehood.Thus, it becomes crucial to identify specific instances of misinformation within the articles.In this research, we investigate a novel task in the field of fake news debunking, which involves detecting sentence-level misinformation.One of the major challenges in this task is the absence of a training dataset with sentence-level annotations regarding veracity.Inspired by the Multiple Instance Learning (MIL) approach, we propose a model called Weakly Supervised Detection of Misinforming Sentences (WSDMS).This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences.We evaluate WSDMS on three real-world benchmarks and demonstrate that it outperforms existing stateof-the-art baselines in debunking fake news at both the sentence and article levels.News Title: NASA Will Pay You 100,000 USD To Stay In Bed For 60 Days!News Article: 𝑠 !: Wouldn't you just love to carry on sleeping on a Monday morning without having to submit to the Monday morning blues and get ready for work?𝑠 " : What type of heaven would you envisage if you were paid to stay in bed 𝑠 # : You can get paid a huge sum of money just staying in bed for two whole months and by you know who, NASA no less!!! yes the American space agency NASA is paying $100,000 to stay in bed for 60 days.𝑠 $ : Most of us dream about hanging out in bed, all day, every day.𝑠 % : NASA is currently on the lookout for people to participate in their "Bed Rest Studies", in which participants will have to stay in bed for 60 days straight.𝑠 & : It does sound like the dream job, right?… 𝑠 ' : You wouldn't just be sleeping you can keep yourself occupied with books, TV, video games, and they can also use their phones as they please… Only $100,000?Not good enough.…

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which is publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art detection baselines and generates high-quality explanations from diverse evaluation perspectives.

Fact checking in product-related community question answering is the task of verifying the truthfulness of an answer towards a given question, where the study has just begun. Most existing related work has focused on tailoring solutions to shallow feature fusion for the single-text claim involved with fact-checked evidence, limiting their success and generality in such answer truthfulness prediction task on E-commerce platforms. In this study, we propose an attention-based hybrid framework for multi-feature interaction fusion to determine the truthfulness of the answer towards a product-related question in E-commerce, which could not only support fine-grained semantic calibration between question-answer pairs for better understanding of the target answers, but also substantially cross-check all retrieved evidence to mine coherent opinions towards the pair. In addition, our framework further integrates non-textual features from metadata for improving performance. Extensive experiments conducted on real-world representative benchmark data show that our proposed model achieves superior performance on the task of answer veracity prediction.

Rumors are rampant in the era of social media.Conversation structures provide valuable clues to differentiate between real and fake claims.However, existing rumor detection methods are either limited to the strict relation of user responses or oversimplify the conversation structure.In this study, to substantially reinforces the interaction of user opinions while alleviating the negative impact imposed by irrelevant posts, we first represent the conversation thread as an undirected interaction graph.We then present a Claim-guided Hierarchical Graph Attention Network for rumor classification, which enhances the representation learning for responsive posts considering the entire social contexts and attends over the posts that can semantically infer the target claim.Extensive experiments on three Twitter datasets demonstrate that our rumor detection method achieves much better performance than stateof-the-art methods and exhibits a superior capacity for detecting rumors at early stages.

Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence labeling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines.