Next-Generation Semantic Multimedia: Generative AI, Human-Centric Personalization, and Digital Sustainability

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 383

Special Issue Editors


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Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: personalization; human–computer interaction; artificial intelligence

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Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece
Interests: social network analysis; multimedia applications; artificial intelligence

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Guest Editor
Department of History, Archaeology and Cultural Resources Management, University of the Peloponnese, 24100 Kalamata, Greece
Interests: cultural informatics; semantics; sustainable development

Special Issue Information

Dear Colleagues,

This Special Issue marks the continued evolution of semantic and social media research, showcasing cutting-edge advancements in generative intelligence, adaptive multimedia, and ethical personalization. It will include extended versions of selected papers from the 21st  International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2026), to be held in late 2026, while also welcoming high-quality original submissions from the broader scientific community.

As we move beyond traditional AI-driven adaptation, the rise of Large Multimodal Models (LMMs) and Generative AI is redefining how digital content is conceived, personalized, and consumed. This Special Issue aims to explore the convergence of semantic technologies with generative workflows, focusing on how these tools can create more intuitive, inclusive, and sustainable digital ecosystems.

We invite contributions that bridge the gap between multimedia computing, cognitive science, and ethical AI. Topics include, but are not limited to, the following:

  • Generative semantic multimedia:
    • Semantic-guided Generative AI for video, audio, and 3D assets;
    • Prompt engineering and latent space manipulation for personalized media;
    • Automated, semantically rich synthetic data generation for training.
  • Next-gen personalization and user modeling:
    • Hyper-personalization via Large Language Models (LLMs) and multimodal agents;
    • Neuro-symbolic approaches to user modeling and intent recognition;
    • Cross-platform identity.
  • Immersive and contextual intelligence:
    • Semantic adaptation in the Industrial Metaverse and XR (VR/AR/MR);
    • Spatial computing and "World Models" for context-aware media;
    • Affective computing: Real-time emotional synchronization in digital twins.
  • Responsibility, ethics, and sustainability:
    • Green AI: Energy-efficient multimedia adaptation and pruning techniques;
    • Detection of deepfakes and AI-generated content (provenance and watermarking);
    • Mitigating algorithmic bias in generative recommendation systems;
    • Human-in-the-loop (HITL) frameworks for trustworthy AI.

Dr. Phivos Mylonas
Dr. Christos Troussas
Dr. Akrivi Krouska
Dr. Manolis Wallace
Dr. Maria Kouri
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multimedia
  • contextual intelligence
  • sustainability
  • user profiling
  • AI
  • semantics

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Published Papers (1 paper)

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22 pages, 2449 KB  
Article
Cross-Linguistic Complexity and Language-Specific Sentiment: Multifractal Structure and Emotional Valence in Popular Music Lyrics Across Three Languages
by Fateme Khanipour, Zeinab Shahbazi, Sara Behnamian, Fatemeh Fogh and Nathan Blood
Computers 2026, 15(5), 315; https://doi.org/10.3390/computers15050315 - 14 May 2026
Viewed by 249
Abstract
We investigate the linguistic complexity and emotional valence of popular song lyrics across English (n=1491), Spanish (n=307), and German (n=225), using an analytical corpus of 2023 tracks drawn from 2113 deduplicated [...] Read more.
We investigate the linguistic complexity and emotional valence of popular song lyrics across English (n=1491), Spanish (n=307), and German (n=225), using an analytical corpus of 2023 tracks drawn from 2113 deduplicated tracks on Spotify’s weekly Top 200 charts (2019–2021). Transformer-based sentiment analysis is combined with complexity-science tools to characterize both the affective content and the structural organization of commercially successful lyrics. A multilingual BERT model reveals a mild negative skew across all three languages (63.7% negative overall); the 1.003-point English–German gap observed under the English-centric VADER lexicon collapses to 0.127 points under BERT, indicating that earlier cross-linguistic sentiment differences are largely measurement artifacts. Word frequency distributions follow Zipf’s law in all three languages (R2>0.96), with English steepest (α=1.409) and German shallowest (α=1.181). Detrended fluctuation analysis indicates persistent long-range correlations (H0.660.76; none of the 50 shuffled surrogates exceeded the observed values), and multifractal singularity spectra are statistically indistinguishable across languages once corpus size is controlled (all pairwise Mann–Whitney p>0.13). Streaming counts within the Top 200 are concentrated (German Gini =0.556) but, given the truncated single-snapshot sample, are reported as within-chart descriptors rather than population-level scaling. Full article
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