  Users frequently express their information needs by means of short and
general queries that are difficult for ranking algorithms to interpret
correctly. However, users' social contexts can offer important additional
information about their information needs which can be leveraged by ranking
algorithms to provide augmented, personalized results. Existing methods mostly
rely on users' individual behavioral data such as clickstream and log data, but
as a result suffer from data sparsity and privacy issues. Here, we propose a
Community Tweets Voting Model (CTVM) to re-rank Google and Yahoo news search
results on the basis of open, large-scale Twitter community data. Experimental
results show that CTVM outperforms baseline rankings from Google and Yahoo for
certain online communities. We propose an application scenario of CTVM and
provide an agenda for further research.
