In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, we identify three distinct developmental patterns for the overall behavior of the articles. We are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, we analyze discussion networks and conclude that edit wars are mainly fought by few editors only.

The choice of natural language technology appropriate for a given language is greatly impacted by density (availability of digitally stored material). More than half of the world speaks medium density languages, yet many of the methods appropriate for high or low density languages yield suboptimal results when applied to the medium density case. In this paper we describe a general methodology for rapidly collecting, building, and aligning parallel corpora for medium density languages, illustrating our main points on the case of Hungarian, Romanian, and Slovenian. We also describe and evaluate the hybrid sentence alignment method we are using. IntroductionThere are only a dozen large languages with a hundred million speakers or more, accounting for about 40% of the world population, and there are over 5,000 small languages with less than half a million speakers, accounting for about 4% (Grimes 2003). In this paper we discuss some ideas about how to build parallel corpora for the five hundred or so medium density languages that lie between these two extremes based on our experience building a 50M word sentence-aligned Hungarian-English parallel corpus. Throughout the paper we illustrate our strategy mainly on Hungarian (14m speakers), also mentioning Romanian (26m speakers), and Slovenian (2m speakers), but we emphasize that the key factor leading the success of our method, a vigorous culture of native language use and (digital) literacy, is by no means restricted to Central European languages. Needless to say, the density of a language (the availability of digitally stored material) is predicted only imperfectly by the population of speakers: major Prakrit or Han dialects, with tens, sometimes hundreds, of million speakers, are low density, while minor populations, such as the Inuktitut, can attain high levels of digital literacy given the political will and a conscious Hansard-building effort (Martin et al 2003). With this caveat, population (or better, GDP) is a very good approximation for density, on a par with web size.The rest of the paper is structured as follows. In Section 1 we describe our methods of corpus collection and preparation. Our hybrid sentence-level aligner is discussed in Section 2. Evaluation is the subject of Section 3.

Of the approximately 7,000 languages spoken today, some 2,500 are generally considered endangered. Here we argue that this consensus figure vastly underestimates the danger of digital language death, in that less than 5% of all languages can still ascend to the digital realm. We present evidence of a massive die-off caused by the digital divide.