This book demonstrates how quantitative methods for text analysis can successfully combine with qualitative methods in the study of different disciplines of the Humanities and Social Sciences (HSS). The book focuses on learning about the evolution of ideas of HSS disciplines through a distant reading of the contents conveyed by scientific literature, in order to retrieve the most relevant topics being debated over time. Quantitative methods, statistical techniques and software packages are used to identify and study the main subject matters of a discipline from raw textual data, both in the past and today. The book also deals with the concept of quality of life of words and aims to foster a discussion about the life cycle of scientific ideas. Textual data retrieved from large corpora pose interesting challenges for any data analysis method and today represent a growing area of research in many fields. New problems emerge from the growing availability of large databases and new methods are needed to retrieve significant information from those large information sources. This book can be used to explain how quantitative methods can be part of the research instrumentation and the "toolbox" of scholars of Humanities and Social Sciences. The book contains numerous examples and a description of the main methods in use, with references to literature and available software. Most of the chapters of the book have been written in a non-technical language for HSS researchers without mathematical, computer or statistical backgrounds.

This book demonstrates how quantitative methods for text analysis can successfully combine with qualitative methods in the study of different disciplines of the Humanities and Social Sciences (HSS). The book focuses on learning about the evolution of ideas of HSS disciplines through a distant reading of the contents conveyed by scientific literature, in order to retrieve the most relevant topics being debated over time. Quantitative methods, statistical techniques and software packages are used to identify and study the main subject matters of a discipline from raw textual data, both in the past and today. The book also deals with the concept of quality of life of words and aims to foster a discussion about the life cycle of scientific ideas. Textual data retrieved from large corpora pose interesting challenges for any data analysis method and today represent a growing area of research in many fields. New problems emerge from the growing availability of large databases and new methods are needed to retrieve significant information from those large information sources. This book can be used to explain how quantitative methods can be part of the research instrumentation and the "toolbox" of scholars of Humanities and Social Sciences. The book contains numerous examples and a description of the main methods in use, with references to literature and available software. Most of the chapters of the book have been written in a non-technical language for HSS researchers without mathematical, computer or statistical backgrounds.--

"How has social psychology investigated the concept of change? In this chapter, we try to answer this question by moving in two directions. First, we briefly consider the main lines of research described in some of the reference books on social psychology, and the contributions of leading scholars who studied change (i.e. the great names in its history, cf. Lubek, 1993). Second, we analyze the abstracts of the papers published in two journals of pivotal importance in this field since their inception, i.e. the Journal of Personality and Social Psychology and the European Journal of Social Psychology. In line with recent developments in digital methods, the distant reading of large corpora of scientific literature can serve as a valid counterpart to more traditional ways of pursuing a historical quest like the one we posit here (Tuzzi, 2018)"--

Millions of scientific articles are published each year, making it difficult to stay abreast of advances within even the smallest subdisciplines. Traditional approaches to the study of science, such as the history and philosophy of science, involve closely reading a relatively small set of journal articles. And yet many questions benefit from casting a wider net: Is most scientific change gradual or revolutionary? What are the key sources of scientific novelty? Over the past several decades, a massive effort to digitize the academic literature and equip computers with algorithms that can distantly read and analyze a digital database has taken us one step closer to answering these questions. The Dynamics of Science brings together a diverse array of contributors to examine the largely unexplored computational frontiers of history and philosophy of science. Together, they reveal how tools and data from automated textual analysis, or machine “reading,” combined with methods and models from game theory and cultural evolutionary theory, can begin to answer fundamental questions about the nature and history of science.

The Third edition of this well-received and widely used Handbook brings together an entirely new set of chapters, to reflect progress and new themes in the ten years to 2022. Building on the established structure of the first two Handbooks, the four sections focus in turn on: philosophy, history and theory development; fresh perspectives on policy and policy development; emerging programs and new approaches; and re-imagining lifelong learning for future challenges. The Handbook stimulates readers with fresh and timely insights, while exploring anew some enduring themes. New topics and themes introduced in all sections address lifelong learning challenges associated with climate change, the digital world, the rise of populism, migration and precarious living. The Handbook features learning innovations and evolving pedagogies such as intergenerational learning, art as pedagogy to promote public-mindedness, neuroscience enhancing learning effectiveness, and lifelong learning for sustainability. Policy responses to lifelong learning for work and well-being are debated. In state of the art contributions, authors from around the globe focus readers' attention on multifaceted processes, issues and decisions that must be better understood and enacted if inclusive development and fair access to lifelong learning are to become realities for us all.

This book presents methods and approaches used to identify the true author of a doubtful document or text excerpt. It provides a broad introduction to all text categorization problems (like authorship attribution, psychological traits of the author, detecting fake news, etc.) grounded in stylistic features. Specifically, machine learning models as valuable tools for verifying hypotheses or revealing significant patterns hidden in datasets are presented in detail. Stylometry is a multi-disciplinary field combining linguistics with both statistics and computer science. The content is divided into three parts. The first, which consists of the first three chapters, offers a general introduction to stylometry, its potential applications and limitations. Further, it introduces the ongoing example used to illustrate the concepts discussed throughout the remainder of the book. The four chapters of the second part are more devoted to computer science with a focus on machine learning models. Their main aim is to explain machine learning models for solving stylometric problems. Several general strategies used to identify, extract, select, and represent stylistic markers are explained. As deep learning represents an active field of research, information on neural network models and word embeddings applied to stylometry is provided, as well as a general introduction to the deep learning approach to solving stylometric questions. In turn, the third part illustrates the application of the previously discussed approaches in real cases: an authorship attribution problem, seeking to discover the secret hand behind the nom de plume Elena Ferrante, an Italian writer known worldwide for her My Brilliant Friend’s saga; author profiling in order to identify whether a set of tweets were generated by a bot or a human being and in this second case, whether it is a man or a woman; and an exploration of stylistic variations over time using US political speeches covering a period of ca. 230 years. A solutions-based approach is adopted throughout the book, and explanations are supported by examples written in R. To complement the main content and discussions on stylometric models and techniques, examples and datasets are freely available at the author’s Github website.