Quotation Rusch, Thomas, Hofmarcher, Paul, Hatzinger, Reinhold, Hornik, Kurt. 2013. Model trees with topic model pre-processing: An approach for data journalism illustrated with the Wikileaks Afghanistan war logs. Annals of Applied Statistics 7 (2): 613-639.


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Abstract

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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Annals of Applied Statistics
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-B
Language English
Title Model trees with topic model pre-processing: An approach for data journalism illustrated with the Wikileaks Afghanistan war logs
Volume 7
Number 2
Year 2013
Page from 613
Page to 639
Reviewed? Y

Associations

People
Rusch, Thomas (Details)
Hofmarcher, Paul (Details)
Hatzinger, Reinhold (Former researcher)
Hornik, Kurt (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Research Institute for Computational Methods FI (Details)
Competence Center for Empirical Research Methods WE (Details)
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