@inproceedings{hristova2012mapping,
  abstract = {Communities of people are better mappers if they are spatially
clustered, as revealed in an interesting new paper by Hristova,
Mashhadi, Quattrone and Capra from UCL. "This preliminary analysis
inspires further inquiry because it shows a clear correlation between
spatial affiliation, the internal community structure and the
community’s engagement in terms of coverage", according to the
authors. They have studied the similarity patterns among eight hundred
contributors to OpenStreetMap, the well-known crowdmapping project and
detected the hidden community structure. It is a very promising field
of research, coupling a social network analysis of crowdsourced data.
Participants to such projects are rarely independent individuals: in
most cases, they involve communities more than single participants and
it would be crucial to uncover how the underlying social structure
reflects on the quantity and the quality of the collected data. It has
the greatest relevance for citizen science projects, as data quality
is often the key issue determining the success or the failure of the
collective effort. },
  author = {Hristova, Desislava and Mashhadi, Afra and Quattrone, Giovanni and Capra, Licia},
  booktitle = {Proc. When the City Meets the Citizen Workshop (WCMCW)},
  interhash = {373e02fe56d30b26261a33135e0b7a45},
  intrahash = {f0a69ac56b94a471b470ebd56545fafd},
  month = jun,
  title = {Mapping Community Engagement with Urban Crowd-Sourcing},
  url = {http://www.cs.ucl.ac.uk/staff/l.capra/publications/wcmcw12.pdf},
  year = 2012
}

@inproceedings{hristova2012mapping,
  abstract = {Communities of people are better mappers if they are spatially clustered, as revealed in an interesting new paper by Hristova, Mashhadi, Quattrone and Capra from UCL. "This preliminary analysis inspires further inquiry because it shows a clear correlation between spatial affiliation, the internal community structure and the community’s engagement in terms of coverage", according to the authors. They have studied the similarity patterns among eight hundred contributors to OpenStreetMap, the well-known crowdmapping project and detected the hidden community structure. It is a very promising field of research, coupling a social network analysis of crowdsourced data. Participants to such projects are rarely independent individuals: in most cases, they involve communities more than single participants and it would be crucial to uncover how the underlying social structure reflects on the quantity and the quality of the collected data. It has the greatest relevance for citizen science projects, as data quality is often the key issue determining the success or the failure of the collective effort. },
  author = {Hristova, Desislava and Mashhadi, Afra and Quattrone, Giovanni and Capra, Licia},
  booktitle = {Proc. When the City Meets the Citizen Workshop (WCMCW)},
  interhash = {373e02fe56d30b26261a33135e0b7a45},
  intrahash = {f0a69ac56b94a471b470ebd56545fafd},
  month = jun,
  title = {Mapping Community Engagement with Urban Crowd-Sourcing},
  url = {http://www.cs.ucl.ac.uk/staff/l.capra/publications/wcmcw12.pdf},
  year = 2012
}

@inproceedings{zanardi2008social,
  abstract = {Social (or folksonomic) tagging has become a very popular way to describe, categorise, search, discover and navigate content within Web 2.0 websites. Unlike taxonomies, which overimpose a hierarchical categorisation of content, folksonomies empower end users by enabling them to freely create and choose the categories (in this case, tags) that best describe some content. However, as tags are informally defined, continually changing, and ungoverned, social tagging has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of users and the noise they introduce. In this paper, we propose Social Ranking, a method that exploits recommender system techniques to increase the efficiency of searches within Web 2.0. We measure users' similarity based on their past tag activity. We infer tags' relationships based on their association to content. We then propose a mechanism to answer a user's query that ranks (recommends) content based on the inferred semantic distance of the query to the tags associated to such content, weighted by the similarity of the querying user to the users who created those tags. A thorough evaluation conducted on the CiteULike dataset demonstrates that Social Ranking neatly improves coverage, while not compromising on accuracy.},
  address = {New York, NY, USA},
  author = {Zanardi, Valentina and Capra, Licia},
  booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
  doi = {http://doi.acm.org/10.1145/1454008.1454018},
  interhash = {dcf815f49a37bf32408fd66ae77d85c3},
  intrahash = {e9e606a98ce7f2fed11c339a500a2f88},
  isbn = {978-1-60558-093-7},
  location = {Lausanne, Switzerland},
  pages = {51--58},
  publisher = {ACM},
  title = {Social ranking: uncovering relevant content using tag-based recommender systems},
  url = {http://portal.acm.org/citation.cfm?id=1454008.1454018},
  year = 2008
}