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Information
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Information Technology in Society
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Information Technology in Society

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2800

Special Issue Editors

Dr. Yen Kheng Tan

E-Mail Website
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:

  1. The role of generative technologies in truth discovery across different domains.
  2. 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 100 words) can be sent to the Editorial Office for announcement on this website.

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

17 pages, 246 KiB  
Article
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 277
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 [...] Read more.
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 KiB  
Article
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
Viewed by 405
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 [...] Read more.
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 KiB  
Article
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
Viewed by 747
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 [...] Read more.
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 KiB  
Article
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 794
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 [...] Read more.
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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