Digital research is taking the humanities by storm. This can be read not only from the many digital humanities programs in education and research in universities around the world but also from the attention for new media practices in humanities and art departments. Famously, and thought-provokingly, media theorist Lev Manovich—strongly rooted in film and media studies—set out to develop a means by which the visual analysis of big data sets of digitized cultural materials could help the study of art and culture transition into the era of big data or, as he calls it, 90the era of “more media” (Manovich, 2009). Often met with scrutiny by art historians, not in favor of a quantitative approach to the arts, Manovich insisted with this “cultural analytics” program on expanding the study of culture by including the vast amounts of user-generated content. As he wrote as early as 2009: “Think about this: the number of images uploaded to Flickr every week is probably larger than all objects contained in all art museums in the world.” Manovich developed the Software Studies Initiative, where he and his team developed software such as Image Plot, for the analysis of large visual data sets. Manovich applies his methods both to digitized materials (such as Time magazine covers) as well as—more recently—to born-digital content (such as selfies on Instagram).
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Content Analysis has been developed within communication science as a technique to analyze bodies of text for features or (recurring) themes, in order to identify cultural indicators, societal trends and issues. And while Content Analysis has seen a tremendous uptake across scientific disciplines, the advent of digital media has presented new challenges to the demarcation and study of content. Within Content Analysis, different strategies have been put forward to grapple with these dynamics. And although these approaches each present ways forward for the analysis of web content, they do not yet regard the vast differences between web platforms that serve content, which each have their own ‘technicities,’ e.g. carry their own (often visually undisclosed) formats and formatting, and output their own results and rankings. In this dissertation I therefore develop Networked Content Analysis as a term for such techniques of Content Analysis that are adapted specifically to the study of networked digital media content. The case in question is climate change, one of the major societal challenges of our times, which I study on the web and with search engines, on Wikipedia as well as Twitter. In all, my contribution provides footing for a return to the roots of Content Analysis and at the same time adds to its toolkit the necessary web- and platform-specific research techniques for creating a fine-grained picture of the climate change debate as it takes place across platforms.
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Climate change is one of the key societal challenges of our times, and its debate takes place across scientific disciplines and into the public realm, traversing platforms, sources, and fields of study. The analysis of such mediated debates has a strong tradition, which started in communication science and has since then been applied across a wide range of academic disciplines.So-called ‘content analysis’ provides a means to study (mass) media content in many media shapes and formats to retrieve signs of the zeitgeist, such as cultural phenomena, representation of certain groups, and the resonance of political viewpoints. In the era of big data and digital culture, in which websites and social media platforms produce massive amounts of content and network this through hyperlinks and social media buttons, content analysis needs to become adaptive to the many ways in which digital platforms and engines handle content.This book introduces Networked Content Analysis as a digital research approach, which offers ways forward for students and researchers who want to work with digital methods and tools to study online content. Besides providing a thorough theoretical framework, the book demonstrates new tools and methods for research through case studies that study the climate change debate with search engines, Twitter, and the encyclopedia project of Wikipedia.
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