The intensity and scale of the opinion fightings in cyberspace on the Russia–Ukraine war (RUW) have opened a new chapter in the history of world warfare. This is a magnificent demonstration of social cognitive war fighting with cyber-physical-social systems (CPSS) that would impact our humankind significantly now and for a long time to come, not just on our understanding of wars, but every aspect of our life. Therefore, it is worth of studying the opinion dynamics of the RUW in the cyberspace. This article will start this direction with an analysis of the evolutionary dynamics of the public opinion fighting, only using Chinese Weibo texts as a case study due to the time constraint. It first clusters the Weibo texts into four categories with unsupervised learning method using Latent Dirichlet Allocation and then collects opinions by extracting keywords. Meanwhile, an opinion adversarial evolution algorithm is proposed to dynamically model the dominance degree of an opinion in the evolutionary processes. We release a dataset of Chinese Weibo associated with RUW. The proposed approach of modeling and analyzing data-driven public opinion dynamics provides a new way for accessing opinion warfare in CPSS.

The well log is the basis for understanding a geologic structure and evaluating petroleum reservoirs. It inevitably leads to missing logs due to borehole conditions and equipment failures. Existing machine-learning methods introduce convolutional neural networks (CNNs) in temporal networks to learn local morphological (spatial) features for improving prediction. However, they ignore the temporal background information of the logs and the differences in spatial features at different depth points. An adaptive spatiotemporal transformer (ASTT) is developed to effectively overcome these challenges, which consists of a temporal background encoder (TBE), a spatial encoder (SE), and a spatiotemporal decoder (STD). TBE introduces average pooling in the transformer encoder to learn the hidden geologic information in the extracted longitudinal temporal features. SE combines CNN and an attention mechanism to learn the spatial features of each depth point differentially. STD maps the extracted spatiotemporal features to the missing logs. Experimental results on real oilfield data indicate that our ASTT achieves excellent performance in terms of fitting degree and test error. The results in the cross-logs and crosswell cases demonstrate the generalization of ASTT.

The dynamics of public opinion on social media affects people’s feeling and minds about international affairs and leads to the reconstruction of societal states for international conflicts. In this article, we analyze the topics’ evolution on social media during the Pelosi visit. Such kind of analysis should help the related departments sense and beware the situation effectively and efficiently, and may provide technical supports for proper policy making and responses. To facilitate this purpose, a new method is proposed and an abbreviated large-graph clustering (ALGC) algorithm has been designed to generate documents and topic representation for alleviating the overhead of high computational complexity of large graphs by reducing the dimensionality of the attention matrix and adjacency matrix. The evolution pattern of topics is also analyzed in and between different time periods. Experiment results show that the proposed method performs well, achieving a high clustering accuracy with lower computational cost. The dataset used in this article is also released for public analysis.

Based on an investigation of online public opinion on the Nahel Merzouk protests in France, an approach for analysing and predicting public opinion on protests based on large language model (LLM) is proposed, revealing the impact of emerging social media on the protests. We demonstrate that protests generate public opinion on social media with some lag, but that comment sentiment and expression are consistent with protest trends. As the protests unfolded, we analyzed the evolution of public sentiment. We constructed the prompt based on historical data to predict the protests using the p-tuning and Lora approach to fine-tune LLM. In addition, we discuss how to use blockchain technology to optimize distributed, self-organizing protests and reduce the potential for disinformation and violent conflict.

Knowledge question and answering (Q&A) is widely used. However, most existing semantic parsing methods in Q&A usually use cascading, which can incur error accumulation. In addition, using only one institution’s Q&A data definitely will limit the Q&A performance, while data privacy prevents sharing between institutions. This article proposes a knowledge graph-based reinforcement federated learning (KGRFL)-based Q&A approach to address these challenges. We design an end-to-end multitask semantic parsing model [MSP-bidirectional and auto-regressive transformers (BART)] that identifies question categories while converting questions into SPARQL statements to improve semantic parsing. Meanwhile, a reinforcement learning (RL)-based model fusion strategy is proposed to improve the effectiveness of federated learning, which enables multi-institution joint modeling and data privacy protection using cross-domain knowledge. In particular, it also reduces the negative impact of low-quality clients on the global model. Furthermore, a prompt learning-based entity disambiguation method is proposed to address the semantic ambiguity problem because of joint modeling. The experiments show that the proposed method performs well on different datasets. The Q&A results of the proposed approach outperform the approach of using only a single institution. Experiments also demonstrate that the proposed approach is resilient to security attacks, which is required for real applications.

Examining the perceptual differences between China and the United States can facilitate a better understanding of their opinions and perspectives, helping to promote peaceful interactions among the two nations as well as the world. This study presents a data-driven approach to measure cognitive differences, investigating these differences from topical and sentimental angles regarding the unmanned balloon event that had been shot down by U.S. warplanes. We also explore the cognitive differences between news media and the followers, and the evolution of topics over time. Our findings reveal those discussions about “balloons” on social media in China and the United States display certain differences in terms of sentiment. In addition, we assess the impact of this event on U.S.–China relationship, particularly in trade. To evaluate the analytical capabilities of the popular ChatGPT model, we use this event as a case study to demonstrate that ChatGPT-like models may have limited capabilities for such kind of specialized analysis. The dataset utilized here is made available for public usage for further investigation on public opinion dynamics for similar events.