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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 22665

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Special Issue Editors

Dr. Yen Kheng Tan

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Guest Editor

School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: energy harvesting; building management systems; inductive power transmission; radiofrequency power transmission; rectennas; DC-DC power convertors; III-V semiconductors; Internet of Things; MIMO systems; biomedical equipment; body sensor networks; circuit tuning; computerised monitoring; condition monitoring; elemental semiconductors; energy conservation; feedforward neural nets; hybrid electric vehicles; invertors; light emitting diodes; lighting control; linear systems; low-power electronics; microcontrollers; neurocontrollers

Dr. Yunhao Jiang

E-Mail Website
Guest Editor

School of Electrical and Electronic Engineering, Hubei University Of Technology, Wuhan 43000, China
Interests: information technology

Special Issue Information

Dear Colleagues,

We are delighted to invite contributions to a Special Issue of Information exploring the transformative impact of information technologies on human beliefs, practices, and truth discovery in the Artificial Intelligence (AI) era. This Special Issue seeks to examine how generative technologies are reshaping the dynamics of knowledge, authority, and societal interaction, particularly in contexts where technology intersects with deeply held values and traditions.

Modern advancements in AI, particularly generative models, offer unprecedented opportunities to influence thought, reduce misinformation, and challenge entrenched worldviews. For instance, AI-powered chatbots have shown potential in mitigating conspiratorial beliefs by facilitating critical dialogs supported by robust evidence. This Special Issue aims to foster a transdisciplinary discussion about these impacts, focusing on two main themes:

The role of generative technologies in truth discovery across different domains.
The evolving relationship between humans and non-human agents, such as AI systems and robots, and the broader implications for societal practices and ethical questions.

We encourage submissions that investigate the integration of information technologies into societal and cultural practices, their potential to shape norms and beliefs, and the emerging challenges they present. Topics of interest include, but are not limited to:

The influence of cyberspace on authority and decision-making.
The role of information technology in reshaping learning practices and knowledge dissemination.
Generative technologies and their socio-cultural entanglements.
The interplay between social media, populism, and authority in the digital era.
Minority voices and inclusivity in the digital sphere.
Conceptualizing the "sacred" in a digital context.
The intersection of artificial intelligence with socioeconomic forces.
Ethical considerations of AI in governance and digital ecosystems.

We welcome single-case studies, comparative analyses, and theoretical contributions that address these themes. This Special Issue aspires to illuminate the profound and multifaceted interactions between information technologies and the quest for truth, fostering a deeper understanding of how AI shapes contemporary society.

We look forward to your submissions and to advancing this critical conversation together.

Dr. Yen Kheng Tan
Dr. Yunhao Jiang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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. Information 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

information technologies
artificial intelligence (AI)
generative models
societal interaction
knowledge dissemination
misinformation
human-AI interaction
ethical implications
cyberspace
authority and decision-making
social media
digital INCLUSIVITY
sacred in digital contexts
AI governance
digital ecosystems

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Published Papers (10 papers)

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Research

24 pages, 572 KB

Open AccessArticle

Can People Tell the Difference Between AI-Generated Mental Health Vignettes? An Exploratory Comparison of User Evaluations

by Vitica X. Arnold, Michael-Erwin P. Abad and Sean D. Young

Information 2026, 17(2), 188; https://doi.org/10.3390/info17020188 - 12 Feb 2026

Viewed by 713

Abstract

Vignettes are brief, descriptive, hypothetical scenarios that have been used to extract attitudes, beliefs, or perceptions from participants across psychology, healthcare, and human–computer interaction. Traditional vignette development is often time and labor-intensive and large language models (LLMs) like ChatGPT-4o may streamline this process. This exploratory between-subjects online survey (n = 66) compared participants’ perceptions of clinically reviewed LLM-generated versus human-written mental health vignettes describing social anxiety, depression, or schizophrenia. Participants rated each vignette on realism, clarity, engagement, emotional impact, perceived likelihood of AI authorship, and likelihood that the target diagnosis applied. Mixed-effects linear regression analyses showed no statistically significant differences between AI-generated and human-written vignettes for any perceived quality rating; estimated source effects were small (|β| ≤ 0.10) with 95% confidence intervals spanning zero across outcomes. Perceived AI authorship likelihood (β = 0.09, 95% CI [−0.22, 0.40]) and correct-diagnosis likelihood ratings (β = −0.07, 95% CI [−0.30, 0.16]) also did not differ by source. Overall, we did not detect statistically significant differences between AI-generated and human-written vignettes. These findings reflect perceptions of AI-generated vignettes that underwent expert clinical review and suggest that LLMs may assist in vignette generation with expert oversight, while highlighting the need for further research on clinical accuracy, diagnostic validity, and generalizability. Full article

(This article belongs to the Special Issue Information Technology in Society)

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16 pages, 2424 KB

Open AccessArticle

Development and Accessibility of the INCE App to Assess the Gut–Brain Axis in Individuals with and Without Autism

by Agustín E. Martínez-González

Information 2026, 17(1), 104; https://doi.org/10.3390/info17010104 - 20 Jan 2026

Viewed by 1078

Abstract

In recent years, there has been increasing interest in the study of the gut–brain axis. Furthermore, there appears to be a relationship between abdominal pain, selective eating patterns, emotional instability, and intestinal disorders in Autism Spectrum Disorder (ASD). This work describes the development and accessibility evaluation of the INCE mobile app. This mobile app allows users to obtain levels of gut–brain interaction severity using two scientifically proven scales: The Gastrointestinal Symptom Severity Scale (GSSS) and the Pain and Sensitivity Reactivity Scale (PSRS). The validity of both instruments was established in previous studies in neurotypical and autistic populations. Statistically significant improvements were found following post-design changes in the use and accessibility of the INCE app (.NET Maui 9 Software) reported by professionals (p = 0.013), families (p = 0.011), and adolescents (p = 0.004). INCE represents an important contribution to evidence-based applications and clearly translates into society. Full article

(This article belongs to the Special Issue Information Technology in Society)

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14 pages, 2628 KB

Open AccessArticle

Artificial Intelligence and International Rules in Cyberspace: A Comparative Knowledge-Mapping Analysis

by Yajuan Liu and Zhi Li

Information 2025, 16(10), 842; https://doi.org/10.3390/info16100842 - 29 Sep 2025

Viewed by 956

Abstract

Considering the new technologies, trends, and geopolitical challenges brought about by the globalization of the Internet, research on international rules in cyberspace holds theoretical urgency, practical significance, and provides guidance for real-world applications. A comparative analysis of relevant papers on the international governance of cyberspace between 1999 and 2020 was conducted using the knowledge mapping tool CiteSpace in Chinese and English databases. The analysis revealed that Chinese research exhibits a stronger focus on national policies, with distinct characteristics at different stages of research. In contrast, English literature demonstrates a clear delineation of the theoretical foundation and maintains a continuous and in-depth exploration of foundational topics. While the field of communication in Chinese has a significantly higher quantity of research compared to English, there exists a structural gap between this field and its foundational theories. In the process of a paradigm shift, it is crucial to emphasize Chinese academic perspectives in this field, pay attention to both domestic and international foundational knowledge and emerging trends, and strengthen theoretical innovation and academic community-building. Full article

(This article belongs to the Special Issue Information Technology in Society)

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14 pages, 1226 KB

Open AccessArticle

Impacts of Artificial Intelligence Development on Humanity and Social Values

by Kelvin C. M. Chong, Yen-Kheng Tan and Xin Zhou

Information 2025, 16(9), 810; https://doi.org/10.3390/info16090810 - 18 Sep 2025

Cited by 2 | Viewed by 5037

Abstract

Today, the impact of information technologies (IT) on humanity in this artificial intelligence (AI) era is vast, transformative, and remarkable, especially on human beliefs, practices, and truth discovery. Modern IT advancements like AI, particularly generative models, offer unprecedented opportunities to influence human thoughts and to challenge our entrenched worldviews. This paper seeks to study the evolving relationship between humans and non-human agents, such as AI systems, and to examine how generative technologies are reshaping the dynamics of knowledge, authority, and societal interaction, particularly in contexts where technology intersects with deeply held social values. In the study, the broader implications for societal practices and ethical questions will be zoomed in for investigation and discussed in the context of moral value as the focus. The paper also seeks to list out the various generative models developed for AI to reason and think logically, reviewed and evaluated for their potential impacts on humanity and social values. Two main research contributions, namely the (1) Virtue Ethics-Based Character Modeling for Artificial Moral Advisor (AMA) and the (2) Direct Preference Optimization (DPO), have been proposed to align contemporary large language models (LLMs) with moral values. The construction approach of the moral dataset that focused on virtue ethics for training the proposed LLM will be presented and discussed. The implementation of the AI moral character representation will be demonstrated in future research work. Full article

(This article belongs to the Special Issue Information Technology in Society)

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13 pages, 382 KB

Open AccessArticle

The Blockchain Trust Paradox: Engineered Trust vs. Experienced Trust in Decentralized Systems

by Scott Keaney and Pierre Berthon

Information 2025, 16(9), 801; https://doi.org/10.3390/info16090801 - 15 Sep 2025

Cited by 2 | Viewed by 2520

Abstract

Blockchain is described as a technology of trust. Its design relies on cryptography, decentralization, and immutability to ensure secure and transparent transactions. Yet users frequently report confusion, frustration, and skepticism when engaging with blockchain applications. This tension is the blockchain trust paradox: while trust is engineered into the technology, trust is not always experienced by its users. Our article examines the paradox through three theoretical perspectives. Socio-Technical Systems (STS) theory highlights how trust emerges from the interaction between technical features and social practices; Technology Acceptance models (TAM and UTAUT) emphasize how perceived usefulness and ease of use shape adoption. Ostrom’s commons governance theory explains how legitimacy and accountability affect trust in decentralized networks. Drawing on recent research in experience design, human–computer interaction, and decentralized governance, the article identifies the barriers that undermine user confidence. These include complex key management, unpredictable transaction costs, and unclear processes for decision-making and dispute resolution. The article offers an integrated framework that links engineered trust with experienced trust. Seven propositions are developed to guide future research and practice. The conclusion argues that blockchain technologies will gain traction if design and governance evolve alongside technical protocols to create systems that are both technically secure and trustworthy in experience. Full article

(This article belongs to the Special Issue Information Technology in Society)

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15 pages, 374 KB

Open AccessArticle

Digital Governance, Democracy and Public Funding Efficiency in the EU-27: Comparative Insights with Emphasis on Greece

by Kyriaki Efthalitsidou, Konstantinos Spinthiropoulos, George Vittas and Nikolaos Sariannidis

Information 2025, 16(9), 795; https://doi.org/10.3390/info16090795 - 12 Sep 2025

Cited by 3 | Viewed by 2263

Abstract

This study explores the relationship between digital governance, democratic quality, and public funding efficiency across the EU-27, with an emphasis on Greece. Using 2023 cross-sectional data from the DESI, Worldwide Governance Indicators, and Eurostat, we apply OLS regression and simulated DEA to assess how digital maturity and democratic engagement impact fiscal performance. The sample includes all 27 EU member states, and the analysis is subject to limitations due to the cross-sectional design and the use of simulated DEA scores. Results show that higher DESI and Voice and Accountability scores are positively associated with greater efficiency. Greece, while improving, remains below the EU average. The novelty of this paper lies in combining econometric regression with efficiency benchmarking, highlighting the interplay of digital and democratic dimensions in fiscal performance. The findings highlight the importance of integrating digital infrastructure with participatory governance to achieve sustainable public finance. Full article

(This article belongs to the Special Issue Information Technology in Society)

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17 pages, 246 KB

Open AccessArticle

The Impact of Information Acquisition on Farmers’ Drought Responses: Evidence from China

by Huiqing Han, Jianqiang Yang and Yingjia Zhang

Information 2025, 16(7), 576; https://doi.org/10.3390/info16070576 - 4 Jul 2025

Viewed by 831

Abstract

Climate change presents major challenges to agriculture, especially in economically underdeveloped regions. In these areas, farmers often lack access to resources and timely information, which limits their ability to respond effectively to drought and threatens agricultural sustainability. This study uses survey data from farmers in underdeveloped regions of China to examine the association between their ability to acquire information and their drought response behaviors. The results indicate that better information acquisition ability is significantly correlated with more effective and scientifically informed decision-making in drought adaptation strategies. To explore the underlying mechanism, we introduce value perception—that is, farmers’ beliefs about the usefulness and benefits of drought adaptation strategies—as a mediating variable. A mechanism model is constructed to test how information acquisition ability relates to behavior indirectly through this perception. We apply a threshold regression model to identify potential nonlinear associations, finding that the relationship between information acquisition ability and drought response behaviors becomes stronger once a certain threshold is surpassed. Additionally, we employ the Item Response Theory (IRT) model to measure the intensity and quality of farmers’ adaptation behaviors more accurately. These findings provide theoretical insights and empirical evidence for enhancing agricultural resilience, while acknowledging that causality cannot be definitively established due to the cross-sectional nature of the data. The study also offers useful guidance for policymakers seeking to strengthen farmers’ access to information, improve value recognition of adaptive actions, and promote sustainable agricultural development in underdeveloped areas. Full article

(This article belongs to the Special Issue Information Technology in Society)

21 pages, 20145 KB

Open AccessArticle

Analyzing Factors Influencing Learning Motivation in Online Virtual Museums Using the S-O-R Model: A Case Study of the National Museum of Natural History

by Jiaying Li, Lin Zhou and Wei Wei

Information 2025, 16(7), 573; https://doi.org/10.3390/info16070573 - 4 Jul 2025

Cited by 5 | Viewed by 2679

Abstract

Advances in information technology have enabled virtual museums to transcend traditional physical boundaries and become important tools in education. Despite their growing use, the factors influencing the effectiveness of virtual museums in enhancing students’ learning motivation remain underexplored. This study investigates key factors that promote learning motivation among secondary school students using the National Museum of Nature’s Online Virtual Exhibition as a case study. Grounded in the Stimulus–Organism–Response (S-O-R) theoretical framework, a conceptual model was developed and empirically tested using Structural Equation Modeling (SEM) to examine relationships among stimulus variables, psychological states, and learning motivation. Results reveal that affective involvement, cognitive engagement, and perceived presence significantly enhance learning motivation, while immersion shows no significant effect. Among the stimulus factors, perceived enjoyment strongly promotes affective involvement, perceived interactivity enhances cognitive engagement, and content quality primarily supports cognitive processing. Visual aesthetics contribute notably to immersion, affective involvement, and perceived presence. These findings elucidate the multidimensional mechanisms through which user experience in virtual museums influences learning motivation. The study provides theoretical and practical implications for designing effective and engaging virtual museum educational environments, thereby supporting sustainable digital learning practices. Full article

(This article belongs to the Special Issue Information Technology in Society)

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25 pages, 1964 KB

Open AccessArticle

Hate Speech Detection and Online Public Opinion Regulation Using Support Vector Machine Algorithm: Application and Impact on Social Media

by Siyuan Li and Zhi Li

Information 2025, 16(5), 344; https://doi.org/10.3390/info16050344 - 24 Apr 2025

Cited by 2 | Viewed by 3318

Abstract

Detecting hate speech in social media is challenging due to its rarity, high-dimensional complexity, and implicit expression via sarcasm or spelling variations, rendering linear models ineffective. In this study, the SVM (Support Vector Machine) algorithm is used to map text features from low-dimensional to high-dimensional space using kernel function techniques to meet complex nonlinear classification challenges. By maximizing the category interval to locate the optimal hyperplane and combining nuclear techniques to implicitly adjust the data distribution, the classification accuracy of hate speech detection is significantly improved. Data collection leverages social media APIs (Application Programming Interface) and customized crawlers with OAuth2.0 authentication and keyword filtering, ensuring relevance. Regular expressions validate data integrity, followed by preprocessing steps such as denoising, stop-word removal, and spelling correction. Word embeddings are generated using Word2Vec’s Skip-gram model, combined with TF-IDF (Term Frequency–Inverse Document Frequency) weighting to capture contextual semantics. A multi-level feature extraction framework integrates sentiment analysis via lexicon-based methods and BERT for advanced sentiment recognition. Experimental evaluations on two datasets demonstrate the SVM model’s effectiveness, achieving accuracies of 90.42% and 92.84%, recall rates of 88.06% and 90.79%, and average inference times of 3.71 ms and 2.96 ms. These results highlight the model’s ability to detect implicit hate speech accurately and efficiently, supporting real-time monitoring. This research contributes to creating a safer online environment by advancing hate speech detection methodologies. Full article

(This article belongs to the Special Issue Information Technology in Society)

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30 pages, 982 KB

Open AccessArticle

Relations of Society Concepts and Religions from Wikipedia Networks

by Klaus M. Frahm and Dima L. Shepelyansky

Information 2025, 16(1), 33; https://doi.org/10.3390/info16010033 - 7 Jan 2025

Viewed by 1769

Abstract

We analyze the Google matrix of directed networks of Wikipedia articles related to eight recent Wikipedia language editions representing different cultures (English, Arabic, German, Spanish, French, Italian, Russian, Chinese). Using the reduced Google matrix algorithm, we determine relations and interactions of 23 society concepts and 17 religions represented by their respective articles for each of the eight editions. The effective Markov transitions are found to be more intense inside the two blocks of society concepts and religions while transitions between the blocks are significantly reduced. We establish five poles of influence for society concepts (Law, Society, Communism, Liberalism, Capitalism) as well as five poles for religions (Christianity, Islam, Buddhism, Hinduism, Chinese folk religion) and determine how they affect other entries. We compute inter-edition correlations for different key quantities providing a quantitative analysis of the differences or the proximity of views of the eight cultures with respect to the selected society concepts and religions. Full article

(This article belongs to the Special Issue Information Technology in Society)

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