‘Where else is the money? A study of innovation in online business models at newspapers in Britain’s 66 cities’

Much like their counterparts in the United States and elsewhere, British newspaper publishers have seen a sharp decline in revenues from traditional sources—print advertising and copy sales—and many are intensifying efforts to generate new income by expanding their online offerings. A study of the largest circulation newspapers in the 66 cities in England, Scotland, Wales and Northern Ireland showed that while only a small minority did not have companion websites, many of the publishers who do have an online presence have transferred familiar revenue models. It has also been recognised that income from these sources is not enough to sustain current operations and innovative publishers have diversified into additional broad categories of Web business models. Significantly, this study did not only compare the approaches of various news publishers with each other, but it also considered how active newspaper publishers were in taking advantage of the variety of business models generally being employed on the Web—and which opportunities were ignored

Efficient multi-label classification for evolving data streams

Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. This paper proposes a new experimental framework for studying multi-label evolving stream classification, and new efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. We present a Multi-label Hoeffding Tree with multilabel classifiers at the leaves as a base classifier. We obtain fast and accurate methods, that are well suited for this challenging multi-label classification streaming task. Using the new experimental framework, we test our methodology by performing an evaluation study on synthetic and real-world datasets. In comparison to well-known batch multi-label methods, we obtain encouraging results