Social bots are virtual accounts controlled by automated programs that can disseminate harmful content on social media and even manipulate public opinion. Social bot detection aims to identify bot accounts, which is crucial for maintaining a healthy online ecosystem. However, advances in multimedia technology and smart device prevalence have diversified user-generated social media content, which now encompasses text, images, videos, and more. Effectively leverage multimodal content for robust social bot detection presents a significant research challenge. Furthermore, existing detection methods often overlook the latent social relationships, which we believe can significantly enhance bot detection. To address the issues, in this paper we propose a novel approach for social bot detection that comprehensively leverages users’ multimodal information. Specifically, we first develop an adaptive multimodal fusion mechanism capable of effectively integrating heterogeneous modal information under imbalanced data distributions to obtain more discriminative user representations. Second, we design a latent social relationship mining algorithm that reconstructs more complete social graphs to enhance the objectivity and completeness of user multimodal representations. Finally, on the basis of our proposed multimodal information fusion mechanism and latent social relationship mining algorithm, we design a new social bot detection model. We conduct extensive experiments on the TwiBot-20 dataset, demonstrating superior performance over baseline methods with significant improvements in both detection accuracy and F1-score. Comprehensive ablation studies and dimensionality-reduced visualizations of user representations further validate the critical role of multimodal information and the effectiveness of our proposed model.

Traditional communication focuses on bit accuracy, while semantic communication improves efficiency by considering the meaning of the data. This paper introduces semantic communication to intelligent transportation systems (ITS), specifically image tasks in vehicle-to-everything (V2X) networks. We propose an adaptive signal-to-noise ratio (SNR) image semantic communication model (ASISC) to address the dynamic nature of V2X environments. An evaluation of the real utility function (ERUF) based on task performance is proposed, which takes into account the semantic transmission rate and the semantic energy consumption. We define the semantic importance scores (SIS) to quantify the complexity of image content. To maximize the ERUF and SIS for image transmission, an optimization problem is formulated to promote high SIS image transmission in V2X networks. In particular, to address the interpretability challenge of neural networks, we propose an independent univariate approach consisting of a content equalization sampling step and an approximate modeling step, to transform the original optimization problem into decoupled power allocation and transmission order subproblems. A ternary search algorithm is used for power allocation, and a distance-based transmission order scheme (DistO) is proposed to give preferential treatment to high SIS image tasks. Simulation results on the CIFAR10 dataset demonstrate that our method outperforms the benchmark schemes, especially in high SIS scenarios, and the transmission efficiency is greatly improved. The proposed scheme is easy to implement and exhibits excellent performance in the V2X networks.

The spread of rumors on social networks can diminish public interests, and even pose a threat to social security. The first prerequisite for effectively suppressing rumors in social networks is the precise detection of rumors. However, factors such as hidden social relationships between users, various social contents, and diverse application scenarios in the vast social network bring severe challenges to the timely and accurate detection of rumors. Therefore, for the sake of exploring the infulence of the above factors, we propose an adaptive rumor detection model combining propagation link prediction (PLP) and semantic-structure adaptive fusion, PLP and semantic-structure adaptive fusion (PLP-SSAF). First, we investigate in depth the impact of hidden social relationships on the propagation structure of posts. We leverage the hidden social relationships between users, and utilize the graph attention neural network to extract propagation structure features that simultaneously include both current and future propagation structure. Second, in order to overcome regional and cultural differences among users in multidialect environment, we enhance the text in the original dataset and merge it with the source text. We use a language representation model to extract semantic features from the joint text, getting joint semantic features that can more comprehensively represent the text. Finally, we propose a parallel features adaptive fusion mechanism that can dynamically update the weights between propagating structure features and semantic features. This enables the obtained post features to better adapt to rumor detection scenarios in social networks under massive data environments. Extensive experiments on three real-world datasets show that our PLP-SSAF significantly improving rumor detection performance in accuracy and generalization over existing methods, and demonstrates superior rumor detection capabilities at early stages.

Intelligent transportation systems (ITS) are transforming modern mobility, with vehicle-to-everything (V2X) communication emerging as a critical technology for enhancing transportation safety and efficiency. However, the dynamic nature of vehicular networks presents significant challenges, including unreliable channel state information and limited spectrum resources. These limitations can compromise the reliable and low-latency transmission of safety-critical data. To address these challenges, this paper proposes a robust approach for device-to-device (D2D)-based V2X communication networks, focusing on jointly optimizing channel reuse and power allocation to maximize information transmission success rate (SR) and average energy efficiency (AE). A two-step strategy is developed: Firstly, a long-timescale Kuhn-Munkres (LTKM) algorithm is devised to improve channel efficiency through intelligent channel reuse decisions. Secondly, the power allocation problem is modeled as a Markov decision process (MDP) and resolved using a proximal policy optimization (PPO)-based algorithm, enhancing the network’s robustness to time-varying vehicular network scenario. Simulation results demonstrate the effectiveness of our proposed method. Compared to the original Kuhn-Munkres algorithm, the signaling overhead of our approach is reduced by approximately 82%. Furthermore, compared to three benchmark schemes, our approach improves overall performance by approximately 11%, 20%, 33%, and 61%, respectively. Moreover, our approach exhibits more stable performance under different vehicle speeds, which further highlights the robustness of the proposed method.

Blended learning networks (BLNs) based on the integration of online learning networks and offline learning environments provide new opportunities and platforms for people to acquire and update useful knowledge and carry out all kinds of learning activities anytime and anywhere. Effective modeling and regulation of the knowledge dissemination process can accurately grasp its dissemination process, promote knowledge innovation and collaborative sharing among learners, and accelerate the maximization of knowledge dissemination. However, it is a challenge to establish a comprehensive dynamics model and adopt the optimal regulation for the knowledge dissemination process under the constraints of a limited budget in large-scale BLNs with diverse learners. To this end, we first explore the evolution process of knowledge dissemination in BLNs and the blended learning interaction process of learners. Based on the system dynamics modeling theory, a dynamics model of knowledge dissemination is established. Second, two kinds of effective regulation strategies are proposed. We establish an optimal regulation system intending to maximize the dissemination of knowledge and use the optimal control theory to tackle the optimal solution distribution of regulation strategies. Then, we propose a knowledge dissemination regulation task allocation method based on the collaborative participation of users, and the reverse auction theory is used to quickly solve the task allocation scheme while ensuring performance. Finally, we demonstrate the effectiveness of proposed models and methods through extensive simulation experiments based on real datasets.

Thanks to the booming development of artificial intelligence, 5G technology, and intelligent manufacturing technology, numerous intelligent edge devices contained in the industrial Internet of Things (IIoT) are endowed with the ability to mine knowledge from perceived massive data. Knowledge-driven IIoT plays an unprecedented role in application fields such as cyber-physical systems and Industry 4.0. However, knowledge is generally scattered across the distributed edge devices of IIoT. Therefore, in order to further achieve the edge intelligence in IIoT, it is very important to explore an efficient edge knowledge sharing method. In this article, we establish a decentralized knowledge sharing platform in IIoT. First, for public knowledge, a dynamics model that can quantitatively describe its sharing process is established by using the system dynamics theory. Furthermore, a control method for maximizing public knowledge sharing under constraints based on the optimal control theory is presented. Second, for private knowledge, a trusted transaction control method based on blockchain technology is proposed. By developing both smart contract and lightweight consensus mechanism, the efficient peer-to-peer sharing of private knowledge is realized, and the integrity of knowledge and the privacy of participants are protected. The results of extensive experiments show that the proposed method can eliminate the obstacles of knowledge sharing among edge devices in IIoT, and further promote the development of edge intelligence empowered 5G-enabled IIoT applications.

The vigorously rising of social media brings a new opportunity for information diffusion in online social networks. However, the existing models of information diffusion only consider the single information, such as rumor. What's more, most of intervention frameworks are modeled under the ideal circumstances without reality constraints. In this article, we propose a novel model of competitive information coupling diffusion to describe the complex process of information diffusion in online social networks. Especially, in order to intervene the process of competitive information coupling diffusion, we introduce three intervention strategies and propose an intervention framework. More importantly, we take the dynamic constraints into consideration such as the budget of intervention and current state of the system, and further propose the constrained intervention model. To reduce the system loss, we establish an optimal control problem with constraints to achieve the optimal allocation of intervention strategies over time and minimize the total loss. We theoretically prove the existence and uniqueness of the optimal solution of the problem, and derive the optimal control solution. Through the experiments, we verify the effectiveness of the model and analyze the efficiency of different intervention strategies about competitive information coupling diffusion with or without constraints, respectively. The results show that the collaborative intervention strategies can effectively impact the process of diffusion and get the minimum system loss. This article provides high realistic significance to the commercial marketing in online social networks.

Mobile social networks (MSNs) provide real-time information services to individuals in social communities through mobile devices. However, due to their high openness and autonomy, MSNs have been suffering from rampant rumors, fraudulent activities, and other types of misuses. To mitigate such threats, it is urgent to control the spread of fraud information. The research challenge is: how to design control strategies to efficiently utilize limited resources and meanwhile minimize individuals' losses caused by fraud information? To this end, we model the fraud information control issue as an optimal control problem, in which the control resources consumption for implementing control strategies and the losses of individuals are jointly taken as a constraint called total cost, and the minimum total cost becomes the objective function. Based on the optimal control theory, we devise the optimal dynamic allocation of control strategies. Besides, a dynamics model for fraud information diffusion is established by considering the uncertain mental state of individuals, we investigate the trend of fraud information diffusion and the stability of the dynamics model. Our simulation study shows that the proposed optimal control strategies can effectively inhibit the diffusion of fraud information while incurring the smallest total cost. Compared with other control strategies, the control effect of the proposed optimal control strategies is about 10% higher.

Routing is one of the most challenging problems in opportunistic networks (OppNets) because of the intermittence of the network connection. To address the issue, many routing schemes have been proposed, however, most of them assume that nodes are willing to forward messages for others. In fact, due to limited resources and poor social relations, nodes in OppNets may be selfish and not reluctant to participate in message forwarding. To tackle this issue, in this paper, we propose a Probabilistic Routing scheme based on Game Theory (PRGT) to stimulate cooperation among selfish nodes. Firstly, we introduce virtual money to buy the message for gaining more profits. Then, according to the historical meeting records among different nodes, we establish a Markov-based probability prediction model, in which the message carrier selects a node with higher probability of encountering the destination node as the relay node. Finally, a game theory approach is employed to simulate trading price for message forwarding. The simulation results demonstrate that our proposed routing scheme can effectively improve the delivery rate of messages and reduce network latency.