Algorithmic recommendation systems shape user exposure by structuring pathways through content and reinforcing sustained engagement within specific regions of the network. Prior work on content traps has primarily emphasized structural dynamics or isolated content signals, limiting the ability to measure how multiple forms of influence jointly contribute to algorithmic entrapment. In this study, we extend the TrapIntensity framework by integrating four components: structural entrapment, measured through attraction and retention dynamics in recommendation networks; persuasion intensity, derived from textual signals grounded in four persuasion theories; transcript-guided keyframe extraction to capture temporally relevant segments of video content; and social, cultural, economic, and political cues extracted from those keyframes. Rather than interpreting higher TrapIntensity scores as stronger trapping in themselves, we treat TrapIntensity as a measurement construct and evaluate whether the inclusion of these additional signals improves its validity. Across fourteen YouTube recommendation-network datasets, we find that the extended formulation improves alignment with engagement and more clearly distinguishes top focal structures from the rest of the network. These findings suggest that measuring content traps requires a joint consideration of structural exposure, persuasion, and visual-symbolic content cues.

Narrative dynamics on social media platforms are shaped not only by content but by the governance regimes that structure their circulation. This study applies the $SEI_AI_DZ$ epidemiological model to examine how competing tariff narratives spread differentially across X, a Western open-access platform, and Weibo, a Chinese government-moderated platform. Analyzing 90,150 Weibo posts and 16,979 X posts collected from February through August 2025, we operationalize the transmission rate ($beta$) and the basic reproduction number ($R_0$) for narratives expressing agreement or disagreement with U.S.-China tariff positions. Results reveal stark platform-specific contagion patterns. On X, both agreeing and disagreeing narratives exhibit suppressed contagion ($R_0 < 1.0$), with near-parity between stances ($R_0 = 0.28$ vs. $0.35$), indicating that open platforms suppress viral spread regardless of narrative alignment. On Weibo, agreeing narratives achieve $R_0 = 1.8$ ($beta = 0.706$) while disagreeing narratives collapse to $R_0 = 0.2$ ($beta = 0.219$), a 190-fold transmission-rate difference. These findings demonstrate that platform governance operates as an epidemiological intervention, structurally enabling or suppressing narrative reproduction at scale, with direct implications for understanding information ecosystems in polarized geopolitical contexts.

Wireless sensing has emerged as a key enabling technology for Integrated Sensing and Communication (ISAC) systems, where radio resources are jointly utilized for environmental sensing and communication. However, under limited radio resources, continuously improving sensing accuracy may lead to inefficient resource utilization once the sensing requirement has been satisfied. To address this issue, this paper proposes a Predictive Marginal Value of Sensing (PM-VoS) based resource allocation framework for dynamic wireless sensing systems. The proposed PM-VoS evaluates the long-term marginal utility of current sensing actions by propagating sensing and non-sensing covariance states over a finite prediction horizon. Based on this metric, a hybrid resource allocation algorithm is developed by combining analytical one-step VoS evaluation with learning-assisted residual predictive value estimation. Simulation results in multi-user multi-target scenarios demonstrate that the proposed scheme reduces sensing position error and improves radio resource utilization efficiency compared with non-predictive VoS-based, heuristic, and random allocation methods.

Authentication and authenticity have been a security challenge since the beginning of information sharing, especially in the context of digital information. With the advancement of generative artificial intelligence, these challenges have evolved, demanding a more up-to-date analysis of their impacts on society and system security. This work presents a mapping review that analyzed 88 documents from the IEEExplorer, Scopus, and ACM databases, promoting an analysis of the resulting portfolio through six guiding questions focusing on the most relevant work, challenges, attack surfaces, threats, proposed solutions, and gaps. Finally, the portfolio articles are analyzed through this guiding research lens and also receive individualized analysis. The results consistently outline the challenges, gaps, and threats related to images, text, audio, and video, thereby supporting new research in the areas of authentication and generative artificial intelligence.