Search (4 results, page 1 of 1)

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Abstract

This paper revisits PoeTryMe, a poetry generation platform, and presents its most recent instantiation for producing poetry inspired by trends in the Twitter social network. The presented system searches for tweets that mention a given topic, extracts the most frequent words in those tweets, and uses them as seeds for the generation of new poems. The set of seeds might still be expanded with semantically-relevant words. Generation is performed by the classic PoeTryMe system, based on a semantic network and a grammar, with a previously used generate&test strategy. Illustrative results are presented using different seed expansion settings. They show that the produced poems use semantically-coherent lines with words that, at the time of generation, were associated with the topic. Resulting poems are not really about the topic, but they are a way of expressing, poetically, what the system knows about the semantic domain set by the topic.

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Abstract

This paper presents a Web intelligence portal that captures and aggregates news and social media coverage about "Game of Thrones", an American drama television series created for the HBO television network based on George R.R. Martin's series of fantasy novels. The system collects content from the Web sites of Anglo-American news media as well as from four social media platforms: Twitter, Facebook, Google+ and YouTube. An interactive dashboard with trend charts and synchronized visual analytics components not only shows how often Game of Thrones events and characters are being mentioned by journalists and viewers, but also provides a real-time account of concepts that are being associated with the unfolding storyline and each new episode. Positive or negative sentiment is computed automatically, which sheds light on the perception of actors and new plot elements.

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Abstract

Purpose Despite the social, educational and therapeutic benefits of book clubs, little is known about which books participants are likely to have read. In response, the purpose of this paper is to investigate the public bookshelves of those that have joined a group within the Goodreads social network site. Design/methodology/approach Books listed as read by members of 50 large English-language Goodreads groups - with a genre focus or other theme - were compiled by author and title. Findings Recent and youth-oriented fiction dominate the 50 books most read by book club members, whilst almost half are works of literature frequently taught at the secondary and postsecondary level (literary classics). Whilst J.K. Rowling is almost ubiquitous (at least 63 per cent as frequently listed as other authors in any group, including groups for other genres), most authors, including Shakespeare (15 per cent), Goulding (6 per cent) and Hemmingway (9 per cent), are little read by some groups. Nor are individual recent literary prize winners or works in languages other than English frequently read. Research limitations/implications Although these results are derived from a single popular website, knowing more about what book club members are likely to have read should help participants, organisers and moderators. For example, recent literary prize winners might be a good choice, given that few members may have read them. Originality/value This is the first large scale study of book group members' reading patterns. Whilst typical reading is likely to vary by group theme and average age, there seems to be a mainly female canon of about 14 authors and 19 books that Goodreads book club members are likely to have read.

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Date

29. 5.2015 19:07:21