Abstract Digital Internet-distributed audiobooks are a surprising game changer in digital publishing. In recent years, audiobooks have moved from being a peripheral by-product of the printed book into the centre of digital publishing and reading, but are typically still ignored in publishing studies. This article raises the voice of the audiobook by giving it a privileged status in the current transformations of book publishing caused by digitization. We present an original model of the digital audiobook circuit, which is based on interviews and knowledge from the Danish market as part of a global industry, which makes it possible to reflect and adjust according to national variations. The model is framed by theoretical discussions of values and dynamics within the digital audiobook circuit in general. Keywords Audio media, audiobook distribution, audiobook production, book circuit, book distribution, book production, digital audiobooks, digital publishing, digital reading, the audiobook circuit

an article by Mark Ledwich (software engineer) and Anna Zaitsev (The University of California, Berkeley, USA) published in First Monday Volume 25 Number 3 (March 2020)

Abstract

The role that YouTube and its behind-the-scenes recommendation algorithm plays in encouraging online radicalisation has been suggested by both journalists and academics alike.

This study directly quantifies these claims by examining the role that YouTube’s algorithm plays in suggesting radicalised content. After categorising nearly 800 political channels, we were able to differentiate between political schemas in order to analyse the algorithm traffic flows out and between each group.

After conducting a detailed analysis of recommendations received by each channel type, we refute the popular radicalisation claims. On the contrary, these data suggest that YouTube’s recommendation algorithm actively discourages viewers from visiting radicalising or extremist content.

Instead, the algorithm is shown to favour mainstream media and cable news content over independent YouTube channels with a slant towards left-leaning or politically neutral channels.

Our study thus suggests that YouTube’s recommendation algorithm fails to promote inflammatory or radicalised content, as previously claimed by several outlets.

Full text (HTML) with lots of graphs and charts to explain the words to people like me who learn best visually.

Labels:
YouTube, recommendation_algorithm, radicalisation,

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