With social media like Facebook, LinkedIN, Twitter and Google+ huge online communities become available. Social media offer new information sources which on their turn bring along some novel challenges. In this part of our research we investigate how crowdsourcing information can be efficiently used for extending existing data sources with complementary data and hence improving information processing tasks like database querying, information retrieval and data analysis. Among others we study:

  • how to estimate the value (in terms of confidence) of crowdsourcing data?
  • how to extract clusters of similar opinions from crowdsourcing data?
  • how to identify users or objects that are in a similar context, based on the answers they provide on inquiries and opinion polls?