This book addresses the application of computing to cultural heritage and the discipline of Digital Humanities that formed around it. Digital Humanities research is transforming how the Human record can be transmitted, shaped, understood, questioned and imagined and it has been ongoing for more than 70 years. However, we have no comprehensive histories of its research trajectory or its disciplinary development. The authors make a first contribution towards remedying this by uncovering, documenting, and analysing a number of the social, intellectual and creative processes that helped to shape this research from the 1950s until the present day. By taking an oral history approach, this book explores questions like, among others, researchers’ earliest memories of encountering computers and the factors that subsequently prompted them to use the computer in Humanities research. Computation and the Humanities will be an essential read for cultural and computing historians, digital humanists and those interested in developments like the digitisation of cultural heritage and artefacts. This book is open access under a CC BY-NC 2.5 license

The first book to intervene in debates on computation in the digital humanities Bringing together leading experts from across North America and Europe, Computational Humanities redirects debates around computation and humanities digital scholarship from dualistic arguments to nuanced discourse centered around theories of knowledge and power. This volume is organized around four questions: Why or why not pursue computational humanities? How do we engage in computational humanities? What can we study using these methods? Who are the stakeholders? Recent advances in technologies for image and sound processing have expanded computational approaches to cultural forms beyond text, and new forms of data, from listservs and code repositories to tweets and other social media content, have enlivened debates about what counts as digital humanities scholarship. Providing case studies of collaborations between humanities-centered and computation-centered researchers, this volume highlights both opportunities and frictions, showing that data and computation are as much about power, prestige, and precarity as they are about p-values. Contributors: Mark Algee-Hewitt, Stanford U; David Bamman, U of California, Berkeley; Kaspar Beelen, U of London; Peter Bell, Philipps U of Marburg; Tobias Blanke, U of Amsterdam; Julia Damerow, Arizona State U; Quinn Dombrowski, Stanford U; Crystal Nicole Eddins, U of Pittsburgh; Abraham Gibson, U of Texas at San Antonio; Tassie Gniady; Crystal Hall, Bowdoin College; Vanessa M. Holden, U of Kentucky; David Kloster, Indiana U; Manfred D. Laubichler, Arizona State U; Katherine McDonough, Lancaster U; Barbara McGillivray, King’s College London; Megan Meredith-Lobay, Simon Fraser U; Federico Nanni, Alan Turing Institute; Fabian Offert, U of California, Santa Barbara; Hannah Ringler, Illinois Institute of Technology; Roopika Risam, Dartmouth College; Joshua D. Rothman, U of Alabama; Benjamin M. Schmidt; Lisa Tagliaferri, Rutgers U; Jeffrey Tharsen, U of Chicago; Marieke van Erp, Royal Netherlands Academy of Arts and Sciences; Lee Zickel, Case Western Reserve U.

This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography, history, linguistics, political science, psychology, public health, and mass communications. The second part of the book provides a panoramic view of the development of big data in the fields of computational social sciences and humanities. The following questions are addressed: why is there a need for novel data governance for this new type of data?, why is big data important for social scientists?, and how will it revolutionize the way social scientists conduct research? With the advent of the information age and technologies such as Web 2.0, ubiquitous computing, wearable devices, and the Internet of Things, digital society has fundamentally changed what we now know as "data", the very use of this data, and what we now call "knowledge". Big data has become the standard in social sciences, and has made these sciences more computational. Big Data in Computational Social Science and Humanities will appeal to graduate students and researchers working in the many subfields of the social sciences and humanities.

This book constitutes the refereed post-proceedings of the Second IFIP WG 12.7 International Workshop on Computational History and Data-Driven Humanities, held in Dublin, Ireland, in May 2016. The 7 full papers presented together with 2 invited talks and 4 lightning talks were carefully reviewed and selected from 14 submissions. The papers focus on the challenge and opportunities of data-driven humanities and cover topics at the interface between computer science, social science, humanities, and mathematics.

Confronting the digital revolution in academia, this book examines the application of new computational techniques and visualisation technologies in the Arts & Humanities. Uniting differing perspectives, leading and emerging scholars discuss the theoretical and practical challenges that computation raises for these disciplines.

This book presents established and state-of-the-art methods in Language Technology (including text mining, corpus linguistics, computational linguistics, and natural language processing), and demonstrates how they can be applied by humanities scholars working with textual data. The landscape of humanities research has recently changed thanks to the proliferation of big data and large textual collections such as Google Books, Early English Books Online, and Project Gutenberg. These resources have yet to be fully explored by new generations of scholars, and the authors argue that Language Technology has a key role to play in the exploration of large-scale textual data. The authors use a series of illustrative examples from various humanistic disciplines (mainly but not exclusively from History, Classics, and Literary Studies) to demonstrate basic and more complex use-case scenarios. This book will be useful to graduate students and researchers in humanistic disciplines working with textual data, including History, Modern Languages, Literary studies, Classics, and Linguistics. This is also a very useful book for anyone teaching or learning Digital Humanities and interested in the basic concepts from computational linguistics, corpus linguistics, and natural language processing.