Thomas Pierre Walter

This survey deals with the problem of evaluating the submissions to crowd sourcing websites on which data is increasing rapidly in both volume and complexity. Usually expert committees are installed to rate submissions, select winners and adjust monetary rewards. Thus, with an increasing number of submissions, this process is getting more complex, time-consuming and hence expensive. In this paper we suggest following text mining methodology, foremost similarity measurements and clustering algorithms, to evaluate the quality of submissions to crowd sourcing contests semi-automatically. We evaluate our approach by comparing text mining based measurement of more than 40'000 submissions with the real-world decisions made by expert committees using Precision and Recall together with F1-score.

The process of writing textbooks is still very traditional regarding the status of authorship and expert opinions. Recently we observe the emergence of authors who follow a different approach, taping the wisdom of crowds as key resource of their own publications. In this paper, we explore business model innovation which leverages value propositions of textbooks by applying crowdsourcing. We use case study research methods to analyze four textbooks written collaboratively. Essential findings indicate occurrence of user-communities fulfilling peer-reviewing, editing or co-authoring despite a lack of monetary incentives. We further detect a tendency towards wiki software providing a community hub. This paper enters the field of partially crowdsourced textbooks and derives future questions of research.