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Social Media Meets AI and Data Science

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1621

Special Issue Editors


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Guest Editor
ADAPT Research Centre, Trinity College Dublin, D02 PN40 Dublin, Ireland
Interests: machine learning (AI); social networks; content engagement; data privacy and ethics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science, University of Pisa, Pisa, Italy
Interests: social networks; decentralization; blockchain; graph analysis

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Guest Editor
Department of Computer Science, University of Pisa, Pisa, Italy
Interests: NFTs; Web3; metaverse; blockchain and cryptocurrencies; decentralized online social networks; peer-to-peer networks; decentralized storages; social network analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, "Social Media Meets AI and Data Science", explores the intersection of social media platforms and advanced computational technologies. Social media platforms generate vast amounts of data daily, offering unique opportunities and challenges for AI and data science applications. This issue aims to present cutting-edge research that utilizes artificial intelligence and data science techniques to analyze, interpret, and leverage social media data. Topics may include but are not limited to, sentiment analysis, user behavior prediction, content personalization, the detection of misinformation, and Trust. Moreover, this issue will delve into ethical considerations, privacy concerns, and the implications of AI-driven social media tools on society and individual users. Contributions may involve various methodologies, including machine learning models, natural language processing, and predictive analytics, providing insights into how these technologies can enhance our understanding and engagement with social media platforms. We invite empirical research, case studies, and review articles that address both the technical aspects and societal impacts of integrating AI and data science in social media contexts.

Dr. Kevin Koidl
Dr. Andrea Michienzi
Dr. Barbara Guidi
Guest Editors

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Keywords

  • artificial intelligence
  • data science
  • social media analytics
  • sentiment analysis
  • user behavior prediction
  • misinformation detection
  • natural language processing
  • machine learning
  • ethical implications
  • privacy concerns
  • trust

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Published Papers (1 paper)

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Research

22 pages, 3983 KiB  
Article
Transforming Education in the AI Era: A Technology–Organization–Environment Framework Inquiry into Public Discourse
by Jinqiao Zhou and Hongfeng Zhang
Appl. Sci. 2025, 15(7), 3886; https://doi.org/10.3390/app15073886 - 2 Apr 2025
Viewed by 580
Abstract
The advent of generative artificial intelligence (GAI) technologies has significantly influenced the educational landscape. However, public perceptions and the underlying emotions toward artificial intelligence-generated content (AIGC) applications in education remain complex issues. To address this issue, this study employs LDA network public opinion [...] Read more.
The advent of generative artificial intelligence (GAI) technologies has significantly influenced the educational landscape. However, public perceptions and the underlying emotions toward artificial intelligence-generated content (AIGC) applications in education remain complex issues. To address this issue, this study employs LDA network public opinion topic mining and SnowNLP sentiment analysis to comprehensively analyze over 40,000 comments collected from multiple social media platforms in China. Through a detailed analysis of the data, this study examines the distribution of positive and negative emotions and identifies six topics. The study further utilizes visual tools such as word clouds and heatmaps to present the research findings. The results indicate that the emotional polarity across all topics is characterized by a predominance of positive emotions over negative ones. Moreover, an analysis of the keywords across the six topics reveals that each has its own emphasis, yet there are overlaps between them. Therefore, this study, through quantitative methods, also reflects the complex interconnections among the elements within the educational ecosystem. Additionally, this study integrates the six identified topics with the Technology–Organization–Environment (TOE) framework to explore the broad impact of AIGC on education from the perspectives of technology, organization, and environment. This research provides a novel perspective on the emotional attitudes and key concerns of the Chinese public regarding the use of AIGC in education. Full article
(This article belongs to the Special Issue Social Media Meets AI and Data Science)
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