  The WikiLeaks Afghanistan war logs contain nearly $77,000$ reports of
incidents in the US-led Afghanistan war, covering the period from January 2004
to December 2009. The recent growth of data on complex social systems and the
potential to derive stories from them has shifted the focus of journalistic and
scientific attention increasingly toward data-driven journalism and
computational social science. In this paper we advocate the usage of modern
statistical methods for problems of data journalism and beyond, which may help
journalistic and scientific work and lead to additional insight. Using the
WikiLeaks Afghanistan war logs for illustration, we present an approach that
builds intelligible statistical models for interpretable segments in the data,
in this case to explore the fatality rates associated with different
circumstances in the Afghanistan war. Our approach combines preprocessing by
Latent Dirichlet Allocation (LDA) with model trees. LDA is used to process the
natural language information contained in each report summary by estimating
latent topics and assigning each report to one of them. Together with other
variables these topic assignments serve as splitting variables for finding
segments in the data to which local statistical models for the reported number
of fatalities are fitted. Segmentation and fitting is carried out with
recursive partitioning of negative binomial distributions. We identify segments
with different fatality rates that correspond to a small number of topics and
other variables as well as their interactions. Furthermore, we carve out the
similarities between segments and connect them to stories that have been
covered in the media. This gives an unprecedented description of the war in
Afghanistan and serves as an example of how data journalism, computational
social science and other areas with interest in database data can benefit from
modern statistical techniques.
