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. (authors' abstract)
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:3926 |
| Date | 06 1900 |
| Creators | Rusch, Thomas, Hofmarcher, Paul, Hatzinger, Reinhold, Hornik, Kurt |
| Publisher | Institute of Mathematical Statistics (IMS) |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf, application/zip, application/pdf |
| Relation | http://dx.doi.org/10.1214/12-AOAS618, http://imstat.org/en/index.html, http://epub.wu.ac.at/3926/ |
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