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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 924

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 1600 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 (2 papers)

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Research

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 174
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 597
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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