  Data crowdsourcing is a data acquisition process where groups of voluntary
contributors feed platforms with highly relevant data ranging from news,
comments, and media to knowledge and classifications. It typically processes
user-generated data streams to provide and refine popular services such as
wikis, collaborative maps, e-commerce sites, and social networks. Nevertheless,
this modus operandi raises severe concerns regarding ill-intentioned data
manipulation in adversarial environments. This paper presents a simulation,
modelling, and classification approach to automatically identify human and
non-human (bots) as well as benign and malign contributors by using data
fabrication to balance classes within experimental data sets, data stream
modelling to build and update contributor profiles and, finally, autonomic data
stream classification. By employing WikiVoyage - a free worldwide wiki travel
guide open to contribution from the general public - as a testbed, our approach
proves to significantly boost the confidence and quality of the classifier by
using a class-balanced data stream, comprising both real and synthetic data.
Our empirical results show that the proposed method distinguishes between
benign and malign bots as well as human contributors with a classification
accuracy of up to 92 %.
