Information Networks with Human-Centric LLMs

A Special Issue of Future Internet (ISSN 1999-5903) belonging to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 20 February 2027 | Viewed by 9942

Editors


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Guest Editor
Department of Psychology and Cognitive Science, University of Trento, 38122 Trento, Italy
Interests: AI psychometrics; cognitive data science; complex networks; cognitive network science; multilayer lexical networks; forma mentis networks; knowledge modeling; natural language processing; social media emotional profiling; data-driven approaches to education/STEM learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Knowledge Discovery and Data Mining Laboratory, Information Science and Technologies Institute, Italian National Research Council, 56124 Pisa, PI, Italy
Interests: complex networks; dynamic networks; community discovery; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Large Language Models (LLMs) have revolutionized our approach to information search and processing, and yet the investigation of LLM–LLM and LLM–human interactions in techno-social systems remains a scarcely explored research area.

Although they are powerful for a variety of purposes, LLMs remain black-box AI models: they can provide accurate classifications or predictions, but with little-to-no justification or interpretative power. Furthermore, although one could examine the behavior of one LLM in isolation, humans or other LLMs interacting together might give rise to unexpected mechanisms like coordination, empathy, or social bonds that could not be observed in individual agents, either human or LLM-based.

Overcoming these two limitations requires next-generation modeling frameworks that account for complex LLM–human interactions and which are capable of capturing the complexity of LLMs as cognitive agents.

This Special Issue aims to bring together quantitative, innovative research in this field. We are open to a variety of publication types, including reviews and theoretical papers, empirical research, computational modeling, and Big Data analyses regarding information networks that feature multiple interactions between LLMs or with humans.

Potential topics include, but are not limited to, the following:

  • Interpretable AI for information processing;
  • Models of network science and LLMs for understanding information flow;
  • Models of knowledge construction and representation in LLM and human systems;
  • Complex systematic approaches to knowledge/information modeling;
  • Trustworthy social and sociable interactions;
  • AI systems versus human social media;
  • AI-based techno-social systems;
  • AI-powered social simulations and agent-based modeling.

Prof. Dr. Massimo Stella
Prof. Dr. Giulio Rossetti
Guest Editors

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Keywords

  • artificial intelligence
  • interpretable AI
  • human-centric AI
  • large language models
  • complex networks
  • network science
  • knowledge modeling
  • data mining
  • intelligent systems

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Published Papers (4 papers)

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Research

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23 pages, 10573 KB  
Article
Reddit Depression Communities as Spaces of Emotion Regulation: A Data-Informed Analysis of Coping and Engagement
by Virginia Morini, Salvatore Citraro, Elena Sajno, Maria Sansoni, Giuseppe Riva, Massimo Stella and Giulio Rossetti
Future Internet 2026, 18(4), 198; https://doi.org/10.3390/fi18040198 - 8 Apr 2026
Cited by 1 | Viewed by 1850
Abstract
Online social platforms increasingly function as informal self-help environments for individuals experiencing depression, offering spaces for emotional expression and peer support outside traditional clinical settings. However, how coping strategies and psychological engagement states—individuals’ emotional and cognitive involvement in managing their condition—are reflected through [...] Read more.
Online social platforms increasingly function as informal self-help environments for individuals experiencing depression, offering spaces for emotional expression and peer support outside traditional clinical settings. However, how coping strategies and psychological engagement states—individuals’ emotional and cognitive involvement in managing their condition—are reflected through online self-disclosure remains poorly understood. We analyzed a large-scale dataset from Reddit depression-related communities to investigate how different psycho-linguistic profiles and coping orientations emerge from users’ language. We collected posts and comments from over 300,000 users across six depression-focused subreddits over two years. User-generated text was characterized through multiple psychological and linguistic dimensions capturing emotions, sentiment, subjectivity, and related features, then aggregated at the user-month level and analyzed using unsupervised clustering techniques. Our analysis identifies four distinct groups characterized by different emotional profiles and dominant coping orientations. These states exhibit meaningful correspondences with established theoretical frameworks, including the Coping Orientations to Problems Experienced model and the Patient Health Engagement model. Our findings demonstrate that large-scale textual data from online communities can provide interpretable insights into coping behaviors and engagement patterns, offering a complementary perspective to traditional approaches for studying mental health. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric LLMs)
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24 pages, 432 KB  
Article
Modelling Large-Scale Group Decision-Making Through Grouping with Large Language Models
by Juan Carlos González-Quesada, José Ramón Trillo, Carlos Porcel, Ignacio Javier Pérez and Francisco Javier Cabrerizo
Future Internet 2025, 17(9), 381; https://doi.org/10.3390/fi17090381 - 25 Aug 2025
Cited by 7 | Viewed by 2335
Abstract
The growing ubiquity of digital platforms has enabled unprecedented participation in large-scale group decision-making processes. Nevertheless, integrating subjective linguistically expressed opinions into structured decision protocols remains a significant challenge. This paper presents a novel framework that leverages the semantic and affective capabilities of [...] Read more.
The growing ubiquity of digital platforms has enabled unprecedented participation in large-scale group decision-making processes. Nevertheless, integrating subjective linguistically expressed opinions into structured decision protocols remains a significant challenge. This paper presents a novel framework that leverages the semantic and affective capabilities of large language models to support large-scale group decision-making tasks by extracting and quantifying experts’ communicative traits—specifically clarity and trust—from natural language input. Based on these traits, participants are clustered into behavioural groups, each of which is assigned a representative preference structure and a weight reflecting its internal cohesion and communicative quality. A sentiment-informed consensus mechanism then aggregates these group-level matrices to form a collective decision outcome. The method enhances scalability and interpretability while preserving the richness of human expression. The results suggest that incorporating behavioural dimensions into large-scale group decision-making via large language models fosters fairer, more balanced, and semantically grounded decisions, offering a promising avenue for next-generation decision-support systems. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric LLMs)
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21 pages, 655 KB  
Article
A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
by Albin Uruqi, Iosif Viktoratos and Athanasios Tsadiras
Future Internet 2025, 17(8), 360; https://doi.org/10.3390/fi17080360 - 8 Aug 2025
Cited by 2 | Viewed by 3836
Abstract
The cold-start problem remains a critical challenge in personalized advertising, where users with limited or no interaction history often receive suboptimal recommendations. This study introduces a novel, three-stage framework that systematically integrates transformer architectures and large language models (LLMs) to improve recommendation accuracy, [...] Read more.
The cold-start problem remains a critical challenge in personalized advertising, where users with limited or no interaction history often receive suboptimal recommendations. This study introduces a novel, three-stage framework that systematically integrates transformer architectures and large language models (LLMs) to improve recommendation accuracy, transparency, and user experience throughout the entire advertising pipeline. The proposed approach begins with transformer-enhanced feature extraction, leveraging self-attention and learned positional encodings to capture deep semantic relationships among users, ads, and context. It then employs an ensemble integration strategy combining enhanced state-of-the-art models with optimized aggregation for robust prediction. Finally, an LLM-driven enhancement module performs semantic reranking, personalized message refinement, and natural language explanation generation while also addressing cold-start scenarios through pre-trained knowledge. The LLM component further supports diversification, fairness-aware ranking, and sentiment sensitivity in order to ensure more relevant, diverse, and ethically grounded recommendations. Extensive experiments on DigiX and Avazu datasets demonstrate notable gains in click-through rate prediction (CTR), while an in-depth real user evaluation showcases improvements in perceived ad relevance, message quality, transparency, and trust. This work advances the state-of-the-art by combining CTR models with interpretability and contextual reasoning. The strengths of the proposed method, such as its innovative integration of components, empirical validation, multifaceted LLM application, and ethical alignment highlight its potential as a robust, future-ready solution for personalized advertising. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric LLMs)
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18 pages, 5771 KB  
Systematic Review
Explainable and Human-Centered AIoT: A Systematic Review of Integration, Interaction, and Impact
by Adolfo A. Jurado Rosas, Marina Fernández Miranda, Gladys L. Peña Pazos, Elberth E. García Panta, Carlos A. Ramos Reyes, Milagros P. Córdova de Chang, José H. Chang Valdiviezo, Olga P. Gamarra Chirinos and Carlos E. Esquerre Aguirre
Future Internet 2026, 18(6), 303; https://doi.org/10.3390/fi18060303 - 4 Jun 2026
Viewed by 848
Abstract
This study analyzes the transition of the Artificial Intelligence of Things (AIoT) toward a Human-Centered Artificial Intelligence (HCAI) approach. Following PRISMA 2020 guidelines, a Systematic Literature Review was conducted on 1 April 2026, retrieving literature from Scopus, Web of Science, SciELO, and Springer [...] Read more.
This study analyzes the transition of the Artificial Intelligence of Things (AIoT) toward a Human-Centered Artificial Intelligence (HCAI) approach. Following PRISMA 2020 guidelines, a Systematic Literature Review was conducted on 1 April 2026, retrieving literature from Scopus, Web of Science, SciELO, and Springer Nature Link. The inclusion criteria prioritized open-access, peer-reviewed English articles published between 2020 and 2025 that addressed AIoT architectures and explainability mechanisms. The screening procedure involved a dual independent review process, followed by a rigorous methodological quality assessment to minimize the risk of bias, culminating in a final sample of 40 studies from an initial pool of 971 records. The findings reveal a structural paradox: while intelligent systems achieve greater operational autonomy, legal and moral accountability remains inexorably bound to the human operator. Furthermore, 77.5% of the evaluated implementations employ superficial explainability, functioning merely as a psychological buffer to manage automation anxiety rather than providing a genuine interactive control mechanism. It is concluded that programming based on HCAI principles must shift from a post hoc feature to an inherent architectural requirement. Establishing explainability by design is imperative to guarantee an interactive audit capability that comprehensively safeguards operational integrity and preserves human agency, although the exclusive reliance on open-access literature limits visibility into proprietary commercial models. Full article
(This article belongs to the Special Issue Information Networks with Human-Centric LLMs)
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