This book introduces quantitative intertextuality, a new approach
to the algorithmic study of information reuse in text, sound and
images. Employing a variety of tools from machine learning, natural
language processing, and computer vision, readers will learn to
trace patterns of reuse across diverse sources for scholarly work
and practical applications. The respective chapters share highly
novel methodological insights in order to guide the reader through
the basics of intertextuality. In Part 1, "Theory", the theoretical
aspects of intertextuality are introduced, leading to a discussion
of how they can be embodied by quantitative methods. In Part 2,
"Practice", specific quantitative methods are described to
establish a set of automated procedures for the practice of
quantitative intertextuality. Each chapter in Part 2 begins with a
general introduction to a major concept (e.g., lexical matching,
sound matching, semantic matching), followed by a case study (e.g.,
detecting allusions to a popular television show in tweets,
quantifying sound reuse in Romantic poetry, identifying influences
in fan faction by thematic matching), and finally the development
of an algorithm that can be used to reveal parallels in the
relevant contexts. Because this book is intended as a "gentle"
introduction, the emphasis is often on simple yet effective
algorithms for a given matching task. A set of exercises is
included at the end of each chapter, giving readers the chance to
explore more cutting-edge solutions and novel aspects to the
material at hand. Additionally, the book's companion website
includes software (R and C++ library code) and all of the source
data for the examples in the book, as well as supplemental content
(slides, high-resolution images, additional results) that may prove
helpful for exploring the different facets of quantitative
intertextuality that are presented in each chapter. Given its
interdisciplinary nature, the book will appeal to a broad audience.
From practitioners specializing in forensics to students of
cultural studies, readers with diverse backgrounds (e.g., in the
social sciences, natural language processing, or computer vision)
will find valuable insights.
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