Interpretation of Crowdsourced Activities Using Provenance Network Analysis


  • Trung Dong Huynh University of Southampton
  • Mark Ebden University of Oxford
  • Matteo Venanzi University of Southampton
  • Sarvapali Ramchurn University of Southampton
  • Stephen Roberts University of Oxford
  • Luc Moreau University of Southampton



provenance, analytics, crowdsourcing, CollabMap, data quality, trust, network analysis, graph


Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. This paper presents an application-independent methodology for analyzing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. Validating this method on the provenance records of CollabMap, an online crowdsourcing mapping application, we demonstrated an accuracy level of over 95% for the trust classification of data generated by the crowd therein.




How to Cite

Huynh, T. D., Ebden, M., Venanzi, M., Ramchurn, S., Roberts, S., & Moreau, L. (2013). Interpretation of Crowdsourced Activities Using Provenance Network Analysis. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 1(1), 78-85.