Abstract-The book translation market is a topic of interest in literary studies, but the reasons why a book is selected for translation are not well understood. The Beyond the Book project investigates whether web resources like Wikipedia can be used to establish the level of cultural bias.This work describes the eScience tools used to estimate the cultural appeal of a book: semantic linking is used to identify key words in the text of the book, and afterwards the revision information from corresponding Wikipedia articles is examined to identify countries that generated a more than average amount of contributions to those articles. Comparison between the number of contributions from two countries on the same set of articles may show with which knowledge the contributors are familiar. We assume a lack of contributions from a country may indicate a gap in the knowledge of readers from that country. We assume that a book dealing with that concept could be more exotic and therefore more appealing for certain readers, while others are therefore less interested in the book. An indication of the 'level of exoticness' thus could help a reader/publisher to decide to read/translate the book or not.Experimental results are presented for four selected books from a set of 564 books written in Dutch or translated into Dutch, assessing their potential appeal for a Canadian audience. A qualitative assessment of quantitative results provides insight into named entities that may indicate a high/low cultural bias towards a book.
When (professional) authors work on their texts, they frequently 'jump' around their document to make textual changes and create new content at a wide range of locations. Currently, a range of linearity measures are available to capture this, some of which requiring time-intensive manual coding. Linearity metrics are commonly calculated based on the leading edge and are mostly used for short texts and single writing sessions. However, especially for longer, multi-session writing processes, text can often be created at various spaces, not necessarily including the leading edge. Accordingly, the leading edge is not enough to distinguish between linear production and non-linear text alterations. Therefore, in the current study, we propose a novel, more flexible, automatized non-linearity analysis, which does not solely rely on the leading edge. In this approach, all backwards and forwards cursor and mouse operations from the point of utterance are extracted from keystroke data, and characterized both based on duration and distance. This results in a detailed list of characteristics per writing episode, allowing us to compare and group episodes of writing at various scales. We illustrate this approach by analysing the writing process of a complete novel based on close to 400 writing sessions totalling 276 h of writing. The results show that the current non-linearity analysis allows us to successfully cluster writing sessions using the non-linearity characteristics. This analysis can be used to find patterns in non-linearity over time, allowing us to chart interactions with the text-produced-so-far and session management strategies in multi-session writing.
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