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

Model trees with topic model preprocessing: An approach for data journalism illustrated with the WikiLeaks Afghanistan war logs