We study whether and how the diversity of editors and teams affects the quality of work in a virtual cooperative work environment on the Wikipedia example. We propose a measure of interests diversity of an editor and some measures of team diversity in terms of members' interests and experience. Statistical and machine learning methods are used to investigate the dependency between diversity and work quality. The presented experimental results confirm our hypothesis that interest diversity of a single editors and team diversity are positively related to the quality of their work. Interestingly, some of our experiments also indicate that diversity may be more important than such attributes as productivity of an editor or size or experience of the team. Our experimental results demonstrate that it is possible to predict work quality based on diversity which is an additional statistical signal that diversity is correlated with work quality.

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Digital media have enormous impact on the public opinion. In the ideal world the news in public media should be presented in a fair and impartial way. In practice the information presented in digital media is often biased and may distort the opinion on a given entity/event or concept. It is important to work on tools that could support the detection and analysis of the information bias. One of the first steps is to study the methods of automatic detection of the articles reporting on the same topic, event or entity to further use them in comparative analysis or building a test or training set.In this paper we report on the experimental results concerning the problem of automatic detection of articles reporting on the same events or entities. We also report some experiments on detecting the source of information based on the content.