Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709

Big Data and Social Media Analytics: A Key to Understanding Human Nature

Big Data and Social Media have transformed knowledge and comprehension in this age of technological advancement. Corporate leaders and professionals in several industries have focused on Big Data, a large collection of data from multiple sources. Meanwhile, social media networks' fast data growth has been lauded as a way to comprehend human behaviours. This study paper examines the critical need to extract intelligent information from the large volume, wide variety, and quick pace of data to meet modern corporate needs. Using specialized tools and procedures for large-scale dataset analysis and effective data management structures are crucial in this context. Big Data and Social Media Analytics offer new insights into human behaviour. This study analyzes how these two fields may work together to create new management strategies. We show that Big Data and Social Media Analytics may provide unmatched opportunities for understanding human behaviour through practical examples and case studies. This integration helps organizations navigate a rapidly changing global market by assessing client preferences, anticipating industry trends, and understanding societal shifts. This study emphasizes the need of using modern technical driving elements to better understand human nature. Integration of several data sources provides insights that give a competitive edge and aid decision-making across sectors. This article examines Big Data and Social Media Analytics, which improves management tactics and deepens understanding of the complex network of human activities and attitudes

Contents and Skills of Data Mining Courses in Analytics Programs

Data Mining (DM) is one of the most offered courses in data analytics education. However, the design and delivery of DM courses present a number of challenges and issues that stem from the DM’s interdisciplinary nature and the industry expectations to generate a broader range of skills from the analytics programs. In this research, we identified and compared frequencies of the contents and skills of DM course syllabi in various data analytics programs. We also identified and systemized DM contents and skills in the analytics job market and compared them with the contents and skills from DM syllabi. Based on these analyses and comparisons, we developed four different templates of the DM contents and skills for a DM course at various levels of the analytics education that include: specialized graduate analytics program (MS), general graduate program (MBA), specialized undergraduate analytics program (BS), and general undergraduate program (BSBA). These templates may be specifically useful for educators to design new or improve existing DM courses in data analytics curricula