Cloud computing, 5G mobile network, and other new technologies have been applied in higher education in recent years. The education video resource service system with adaptive multiterminals has received widespread attention from the field. From these videos, students can get new knowledge and use the system’s built-in text comment function to communicate and interact with others. However, due to the fast increase in the number of such text comments, the traditional text content moderation methods such as the keyword method and the regular expression method can no longer meet the growing business needs. Therefore, to solve this matter, this study designed a text content moderation (TCM) system based on artificial intelligence (AI), which uses artificial intelligence and cloud-based algorithm models to analyse and recognize the text comments submitted from the web-end and app-end of the education video resource service system and completes operations such as automatic detection and manual moderation. The proposed TCM system can significantly improve the efficiency of text content moderation.

In recent years, many new technologies, such as the Internet, cloud computing, and the 5th-generation (5G) mobile network, have been widely applied in colleges. The structure and functions of traditional media technology can no longer satisfy the demand for media resource management (MRM) in colleges. Therefore, this paper creates an intelligent MRM system based on cloud service, providing a brand-new intelligent MRM solution. Through cloud service and artificial intelligence (AI), the proposed system relies on intelligent algorithms to realize automatic, comprehensive, and quick identification, review, tagging, storage and query of videos and images in media resources. The research findings can effectively improve the quality of college teaching and enhance the value of media resources.

With the rapid growth of mobile media, large quantities of mobile content have been generated by moving entities. Some content generation patterns become popular for users and professional organizations, e.g., user-generated content, professionally-generated content, machine-generated content. However, due to the limitations of device types and functions, it is necessary to explore the new production tools and further inspire the content potential in mobile scenarios. According to the production capacity supported by Internet of Vehicles, intelligent connected vehicles (ICVs) emerge as new content generated devices in sustainable cities. In this paper, we propose the concept of Mobile-Generated Content (MoGC) as a new part of production patterns. First, we analyze the relationship between MoGC and existing patterns from the perspectives of entity and workflow. Second, the unique functionality and social property of MoGC are revealed, i.e., ICVs play the role of middle platform with data and technology offices. The current dominant discourse created by the professional institutions (e.g., media agency or governing authority) will transfer to the vehicle users. In this way, the content generation system has been further enriched in omni-media environments through efficiently integrating productivity tools and resources. Besides, MoGC not only contributes to social governance by enlarging the news source and coverage, but also strengthen the personal discourse in mobile scenarios.

Reliable load forecasting is essential for electricity generation and even for people’s lives. However, the existing load forecasting theories cannot match the requirements of complex systems (e.g., smart grids). Deleuze’s metaphysical complexity theory is seen as the theoretical foundation for comprehending complex systems, and thus, a new perspective based on Deleuze’s assemblage concept is given. According to the assemblage perspective, the electrical load is a quantitative representation of the mutual becoming of people and electricity, and load forecasting is an attempt to control this continuous process of deterritorialization and reterritorialization. We built an LSTM-RNN-FNN model for load forecasting based on the assemblage perspective, and the assessment results demonstrated that the model has high prediction performance. Furthermore, the performance of adding the temperature parameter into the network is also tested, while the correlation between the temperature and load is not strong enough and may not be suitable for load prediction. The assemblage perspective has significant implications for future load forecasting and potentially smart grid research.

This study investigates how algorithmic infrastructures on Chinese social media govern visibility not through post-hoc deletion but through pre-emptive design. Focusing on Xiaohongshu during the 2025 China—U.S. trade conflict, it constructs a three-part comparative corpus—over 30,000 political comments from Xiaohongshu, 5000 non-political comments from the same platform, and 1200 Weibo comments on the same issue. A multi-stage diagnostic framework integrates weakly supervised anomaly detection and clustering, behavioral—semantic network analysis, topic modeling (Latent Dirichlet Allocation, LDA), and sentiment analysis to trace how expressive diversity is filtered across platforms. The results reveal no evidence of coordinated automation or bot activity but demonstrate a pronounced convergence in tone and theme: Xiaohongshu's political discourse is affectively positive, rhetorically moderate, and thematically compressed. In contrast, its non-political baseline displays greater affective heterogeneity, while Weibo's corpus shows a wider distribution of topics and discursive framings despite a similarly positive sentiment bias. These contrasts indicate that discursive alignment on Xiaohongshu arises not from manipulation or censorship, but from infrastructural filtration—ranking algorithms, participation thresholds, and affective heuristics that quietly define what can surface. The study conceptualizes this anticipatory logic as discursive preclusion, a form of algorithmic governance that renders dissent statistically improbable rather than overtly suppressed. Methodologically, it advances a “detection-to-diagnosis” approach that interprets silence and convergence as evidence of design-based control; conceptually, it reframes platform power as the governance of legibility and affect rather than of speech itself.

In the digital age, online public opinion plays a pivotal role in shaping social stability, policymaking, and public trust in institutions. Given the frequent occurrence of public opinion crises, it is imperative to explore their evolutionary dynamics and effective mitigation strategies. This study develops a three-party evolutionary game model involving the government, ordinary netizens, and media/KOLs, incorporating both inter-group strategy influence and intra-group incentive effects. The model enhances traditional replicator dynamics by embedding incentive coefficients that reflect the strategic suppressive or promotive effects within each group. Simulation results reveal that changes in incentive structures significantly affect the speed and stability of opinion convergence. For instance, when the media's suppression of dissemination strategies is strong ([Formula: see text]), all groups reach near-equilibrium within 3-5 time steps, with netizen participation stabilizing above 0.99 by t = 3. However, when only the government's suppressive influence increases ([Formula: see text]), convergence is slower and displays diminishing returns. As [Formula: see text] continues to rise, netizen responsiveness plateaus, indicating a saturation effect whereby excessive suppression loses effectiveness in accelerating stabilization. These findings challenge the assumption that earlier or stronger intervention is inherently more effective. Instead, they underscore the importance of calibrated timing and intensity, as public sentiment evolves through the interplay of government response, media coordination, and audience receptiveness. Netizens respond more rapidly than institutional actors, reflecting their sensitivity to perceived information gaps. Effective mitigation of negative sentiment thus requires not only timely action but also adaptive adjustment of strategic influence in accordance with systemic feedback.