Detecting rumors on social media is challenging when posts are semantically underspecified and discussion threads are noisy or polarized, which can encourage detectors to exploit spurious correlations. We propose CLEAR (Contextual Potential Alignment Capture Network), an evidence-grounded framework that models hierarchical comment dynamics and incorporates auxiliary LLM-based veracity assessments for credibility-aware prediction. CLEAR couples prototype-conditioned flow purification with Dirichlet evidential learning to derive geometry-grounded evidence for calibrated inference. We further introduce an entropy-adaptive Hard-Shift reweighting strategy to suppress noise-driven shortcuts. Experiments on Weibo-19 (2927 samples) and PHEME (2018 samples) show that CLEAR achieves 93.16% and 91.56% accuracy, outperforming the average strong recent baselines by 3.2 and 5.5 percentage points, respectively. To stress-test generalization under distribution shift, we curate VRDD with 4020 posts (2348 non-rumors and 1672 rumors), a boundary-dense benchmark that emphasizes vague content. Results confirm CLEAR’s robustness to evolving rumor patterns and highlight the curriculum-dependent effect of reweighting.

Fake news detection is a critical challenge in the digital age, as the fabricated content rapidly undermines public trust across diverse domains. Existing methods rely on domain-agnostic feature extractors, which struggle to decouple heterogeneous features and suffer from semantic entanglement, reducing cross-domain generalization. Moreover, static architectures with fixed knowledge representations cannot adapt to evolving misinformation patterns during events such as public health crises and political upheavals. Conventional multi-domain frameworks also incur high computational costs because of redundant parameter scaling, hindering near real-time deployment. To address these issues, herein, we propose a D ual-layer H eterogeneous E xpert network and dynamic M emory augmentation for multi-domain F ake N ews D etection (DHEM-FND). First, a heterogeneous expert network disentangles domain-specific semantics through three pathways: (i) a multiscale convolutional neural network capturing local–global patterns, (ii) emotion-aware multilayer perceptron detecting sentiment–context discrepancies, (iii) and self-attention module with statistical priors identifying stylistic fingerprints. Orthogonal constraints minimize feature leakage across domains. Second, a dynamic memory augmentation mechanism tracks evolving fake news tactics by maintaining hierarchical domain prototypes, incrementally updated using attention-based alignment. Third, sparse gating and parameter reuse strategies optimize efficiency by activating only the top-k relevant experts during inference while freezing 75% of backbone parameters. We validated the DHEM model using four real-world datasets: Twitter15, Twitter16, Weibo21, and FineFake. We achieved an average detection accuracy of 92.57%, while reducing cross-domain interference by 41.2%, improving detection speed by 17.2%. Thus, the superior performance of the proposed method in dealing with evolving fake news and scalability is demonstrated.

Sequential recommendation systems are often misled by large-scale traffic data containing misinformation, which can trigger impulsive buying and reduce prediction accuracy. However, most existing methods either focus solely on long-term user preferences or assume a smooth evolution of user intentions, resulting in inadequate modeling and poor performance. This paper proposes an in-depth propaganda strategy by proposing an impulsive buying-aware model based on users’ long-term and short-term preference representation for sequential recommendation (INSPEQ), aiming to mitigate misinformation effects and enhance recommendation quality. INSPEQ first distinguishes long- and short-term preferences by extracting item attributes embedded in both intrinsic and extrinsic knowledge through relation paths. For long-term behavior modeling, a PATR-GRU network with apGRUs is used to learn stable user preferences by leveraging persistent item attributes and encoding temporal dependencies between adjacent and nonadjacent items along knowledge sequences, capturing both direct and indirect temporal influences. To capture short-term user preferences, a modified self-attention mechanism is introduced, enhanced with time-aware positional encoding, enabling the model to reflect recent shifts in user behavior more effectively within dynamic sessions. These dual representations are then adaptively fused via an MLP-based gated mechanism, assigning dynamic weights based on user impulsivity levels to flexibly balance stability and recency in decision-making. Extensive experiments on three real-world datasets demonstrate that INSPEQ consistently outperforms nineteen state-of-the-art methods. Specifically, it achieves up to +10.1 % in nDCG@5 and +11.4 % in HitRatio@5 over the strongest baselines, highlighting the effectiveness of jointly modeling preference dynamics while alleviating misinformation effects in practical recommendation environments.

The fast dissemination of online information has facilitated the evolution of multimodal fake news, thereby rendering the trustworthiness of its content difficult to identify. Some existing studies, although capturing the consistency and inconsistency features between different modalities, neglect to dynamically balance these two types of features based on their contributions during the features fusion. Thus, we propose a S emantic R elationship-based C onsistency and I nconsistency B alancing N etwork for multimodal fake news detection (SR-CIBN). Specifically, the global features are thoroughly investigated by hierarchically penetrating and interacting between multimodal features that are aligned by contrastive learning at both the intra- and inter-modal views. Then, the global consistency and inconsistency features are obtained through the interaction between the selected key image patches features and the global features. Additionally, the fusion intensity of the global consistency and inconsistency features is adjusted based on the image–text matching degree, resulting in the final optimized features. Under the joint learning framework we proposed, confusion between semantically similar real and fake news is effectively avoided by training with triplet loss based on the image–text semantic relationship. Our model surpasses comparable approaches, as shown by comprehensive experiments on the Twitter and Weibo datasets.

The abundance of heavy data on social media enables users to share opinions freely, leading to the rapid spread of misleading content. However, existing fake news detection methods exaggerate the influence of public opinions, making it challenging to combat misinformation since its early spreading state. To tackle this issue, we propose a novel fake news detection framework through news semantic environment perception (NSEP) to identify fake news content. The NSEP framework consists of three major steps. First, NSEP divides the news semantic environment with time-constrained intervals into macro and micro semantic environments using an in-depth distinguisher module. Second, graph convolutional networks are applied to perceive the semantic inconsistencies between intrinsic news content and extrinsic post tokens in the macro semantic environment. Third, a micro semantic detection module guided by multihead attention and sparse attention is utilized to capture the semantic contradictions between news content and posts in the micro semantic environment, providing explicit evidence for determining the authenticity of fake news candidates. Empirical experiments conducted on real-world Chinese and English datasets show that the NSEP framework on Chinese datasets achieved as high as 86.8% accuracy, performing at most 14.1% higher accuracy than that of other state-of-the-art baseline methods and confirming that detecting news content through both micro and macro semantic environments is an effective methodology for alleviating early propagation of fake news. The findings also comprehensively indicate that both news items and posts are critical for the early debunking of fake news and in theories concerning information science.

Multimodal fake news detection methods based on semantic information have achieved great success. However, these methods only exploit the deep features of multimodal information, which leads to a large loss of valid information at the shallow level. To address this problem, we propose a progressive fusion network (MPFN) for multimodal disinformation detection, which captures the representational information of each modality at different levels and achieves fusion between modalities at the same level and at different levels by means of a mixer to establish a strong connection between the modalities. Specifically, we use a transformer structure, which is effective in computer vision tasks, as a visual feature extractor to gradually sample features at different levels and combine features obtained from a text feature extractor and image frequency domain information at different levels for fine-grained modeling. In addition, we design a feature fusion approach to better establish connections between modalities, which can further improve the performance and thus surpass other network structures in the literature. We conducted extensive experiments on two real datasets, Weibo and Twitter, where our method achieved 83.3% accuracy on the Twitter dataset, which has increased by at least 4.3% compared to other state-of-the-art methods. This demonstrates the effectiveness of MPFN for identifying fake news, and the method reaches a relatively advanced level by combining different levels of information from each modality and a powerful modality fusion method.

Privacy issues have become a major concern in the web of resource sharing, and users often have difficulty managing their information disclosure in the context of high-quality experiences from social media and Internet of Things. Recent studies have shown that users’ disclosure decisions may be influenced by heuristics from the crowds, leading to inconsistency in the disclosure volumes and reduction of the prediction accuracy. Therefore, an analysis of why this influence occurs and how to optimize the user experience is highly important. We propose a novel heuristic model that defines the data structures of items and participants in social media, utilizes a modified decision-tree classifier that can predict participants’ disclosures, and puts forward a correlation analysis for detecting disclosure inconsistences. The heuristic model is applied to real-time dataset to evaluate the behavioral effects. Decision-tree classifier and correlation analysis indeed prove that some participants’ behaviors in information disclosures became decreasingly correlated during item requesting. Participants can be “persuaded” to change their disclosure behaviors, and the users’ answers to the mildly sensitive items tend to be more variable and less predictable. Using this approach, recommender systems in social media can thus know the users better and provide service with higher prediction accuracy.

For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self‐disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single‐edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.

Evaluation of the Deep Web data sources must be based on the data in the Web databases, then how to select the most representative keywords as a query word to obtain a large number of uniformly distributed data is a major difficulty, this paper proposed a Deep Web database sampling method based on high correlation keyword, using a graph based keyword-connected network to get query words, the method can get a random sample of high-quality data from the Deep Web data source more efficiently.

A modified collaborative filtering recommendation has been put forward for clustered data in social networks. Firstly, the basic model is built up, which shows the outline of current existing types of the recommendation platform. Secondly, on this basis, the vertexes and edges in the model are divided as the entities of the social networks with properties given formally. This framework implements traditional collaborative filtering based on clustered datasets using improved k-means clustering method, and our design has put forward to picking up the active users among all of them and let them help our system generate recommendations more precise and faster. Finally, we have crawled through data from two famous movie recommendation websites, MovieLens and Imdb, in the experiment test. From the experiment results, it is concluded that the implemented collaborative filtering recommendation system is performed better than the naive one both in precision and stability, which aims to supply collaborative filtering recommendation and actively contributing recommendation that suits users' tastes, has completed the task of improving collaborative recommendation and reached the expected goals.