In today's world, news events are emerging incessantly. Classifying the same event reported by different countries is of significant importance for public opinion control and intelligence gathering. Due to the diverse types of news, relying solely on translators would be costly and inefficient, while depending solely on translation systems would incur considerable performance overheads in invoking translation interfaces and storing translated texts. To address this issue, we mainly focus on the clustering problem of cross-lingual news. To be specific, we use a combination of sentence vector representations of news headlines in a mixed semantic space and the topic probability distributions of news content to represent a news article. In the training of cross-lingual models, we employ knowledge distillation techniques to fit two semantic spaces into a mixed semantic space. We abandon traditional static clustering methods like K-Means and AGNES in favor of the incremental clustering algorithm Single-Pass, which we further modify to better suit cross-lingual news clustering scenarios. Our main contributions are as follows: (1) We adopt the English standard BERT as the teacher model and XLM-Roberta as the student model, training a cross-lingual model through knowledge distillation that can represent sentence-level bilingual texts in both Chinese and English. (2) We use the LDA topic model to represent news as a combination of cross-lingual vectors for headlines and topic probability distributions for content, introducing concepts such as topic similarity to address the cross-lingual issue in news content representation. (3) We adapt the Single-Pass clustering algorithm for the news context to make it more applicable. Our optimizations of Single-Pass include adjusting the distance algorithm between samples and clusters, adding cluster merging operations, and incorporating a news time parameter.

While applying stochastic alternating direction method of multiplier (ADMM) methods has become enormously potential in distributed applications, improving the algorithmic flexibility can bring huge benefits. In this paper, we propose a novel stochastic optimization method based on distributed ADMM method, called Flex-SADMM. Specifically, we incorporate the variance reduced first-order information and the approximated second-order information for solving the subproblem of ADMM, which targets at the stable convergence and improving the accuracy of the search direction. Moreover, different from most ADMM based methods that require each computation node to perform the update in each iteration, we only require each computation node updates within a bounded iteration interval, this has significantly improved the flexibility. We further provide the theoretical results to guarantee the convergence of Flex-SADMM in the nonconvex optimization problems. These results show that our proposed method can successfully overcome the above challenges while the computational complexity is maintained low. In the empirical study, we have verified the effectiveness and the improved flexibility of our proposed method.

With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech recognition, machine translation, image processing, and the creation of recommendation systems. Therefore, many re-al-world complex problems can be solved by the deep learning techniques. As is known, traditional news recommendation systems mostly employ techniques based on collaborative filtering and deep learning, but the performance of these algorithms is constrained by the sparsity of the data and the scalability of the approaches. In this paper, we propose a recommendation model using granular neural network model to recommend news to appropriate channels by analyzing the properties of news. Specifically, a specified neural network serves as the foundation for the granular neural network that the model is considered to be build. Different information granularities are attributed to various types of news material, and different information granularities are released between networks in various ways. When processing data, granular output is created, which is compared to the interval values pre-set on various plat-forms and used to quantify the analysis's effectiveness. The analysis results could help the media to match the proper news in depth, maximize the public attention of the news and the utilization of media resources.

Social bots threaten the integrity of online social platforms by spreading misinformation, manipulating public opinion, and carrying out fraudulent activities. Detecting social bots is therefore not just a classification problem: looking ahead, a detector can also act as a discriminative component in agent-based social network generation, supplying realism-assessment signals for a “Generate–Detect–Explain–Refine” loop. Existing graph-based detection methods rely mainly on social relation graphs and do not explicitly treat behavioral similarity as a separate parallel graph branch. This paper proposes a dual-branch graph neural network for Twitter bot detection in which a heterogeneous social relation graph is accompanied by a sparse, conditionally active behavior-similarity auxiliary view; the paper’s main contributions are a competitive detector and a diagnostic protocol for attributing and repairing the value of that auxiliary view. The relation branch uses a Relational Graph Convolution Network to process a heterogeneous social relation graph, while the auxiliary branch applies a Graph Attention Network to a behavior similarity graph that links users with similar follower–following ratios, tweet type distributions, and post influence metrics. Experiments on TwiBot-22 show that the proposed model reaches 89.48 ± 0.07% accuracy and 78.11 ± 0.14% macro-F1 over five random seeds at a per-seed validation-calibrated decision threshold (89.92 ± 0.07% accuracy and 77.28 ± 0.31% macro-F1 at the default 0.5 threshold). Per-class and threshold analyses further show that the default operating point is precision-oriented on the minority bot class (bot recall 54.83 ± 1.60%), while operating near the validation-calibrated threshold (τ = 0.40) raises bot recall to 64.12 ± 1.31%. Ablation studies show that the semantic text embedding provides the largest single performance contribution and that the relation branch supplies the bulk of the graph signal. A degree-stratified analysis further shows that the behavior branch is functionally active on its ≈1% covered users—2.9× more prediction changes and 4.0× larger probability shifts than on uncovered nodes—where a validation-only subgroup threshold lifts macro-F1 from 51.85 to 67.17 without retraining, so that it can be turned into a deployable calibrated component.