Abstract
Political event data have long been used in the quantitative study of international politics, dating back to the early efforts of Edward Azar’s COPDAB [1] andCharles McClelland’s WEIS [18] as well as a variety of more specialized efforts such as Leng’s BCOW [16]. By the late 1980s, the NSF-funded Data Development in International Relations project [20] had identified event data as the second most common form of data—behind the various Correlates of War data sets— used in quantitative studies. The 1990s saw the development of two practical automated event data coding systems, the NSF-funded KEDS (http://eventdata. psu.edu; [9, 31, 33]) and the proprietary VRA-Reader (http://vranet.com; [15, 27]) and in the 2000s, the development of two new political event coding ontologies— CAMEO [34] and IDEA[4,27]—designed for implementation in automated coding systems. A summary of the current status of political event projects, as well as detailed discussions of some of these, can be found in [10, 32].
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Notes
- 1.
Individual coders, particularly working for short periods of time, can of course reliably code much faster than this. But for the overall labor requirements—that is, the total time invested in the enterprise divided by the resulting useable events—the six events per hour is a pretty good rule of thumb and—like the labor requirements of a string quartet—has changed little over time.
- 2.
The count of “stories” has varied continually as we’ve updated the downloads, modified the filters and so forth, and so an exact count is both unavailable and irrelevant. But starts around around eight to nine-million.
- 3.
We’ve actually identified about 75 distinct sources in the stories, presumably the result of quirks in the LN search engine. However, these additional sources generate only a small number of stories, and by far the bulk of the stories come from the sources we had deliberately identified.
- 4.
This will not, however, catching spelling corrections in the first 48 characters. In the Reuters-based filtering for the KEDS project, we did a count of the frequency of letters in the lead sentence, and identified a duplicate if the absolute distance between that vector for two stories, ∑ | x i − y i | > η, where the threshold η was usually around 10. This catches spelling and date corrections, the most common source of duplicates in Reuters, but failed on AFP, which tends to expand the details in a sentence as more information becomes available.
- 5.
Notably to traders—carbon-based and silicon-based—in the financial sector, which drives much if not most of the international reporting. The likelihood of an event being reported is very much proportional to the possibility that someone can make or lose money on it.
- 6.
The phrase “cue category” refers to the broad two-digit codes, as opposed to the more specific three and four digit subcategories.
- 7.
To date, all of the successful automated event data coding systems are dictionary and rule based, rather than using statistical-methods: see [36]. While statistical methods would certainly be attractive, and seem to work on highly simplified “toy problems” such as those in [6], all of the successfully-deployed systems to date are dictionary-based, and numerous efforts to scale initially-promising statistical methods have failed.
- 8.
Including, at the request of the sponsor, some bugs in TABARI, though after the equivalence of the two systems was demonstrated, these were corrected in both systems.
- 9.
In principle these enhancements could also be applied to Jabari-NLP, though it is running in secure military systems rather than open environments and to date has made less use of cluster processing.
- 10.
Though we’ve not been able to locate this on the web. Itself interesting.
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Acknowledgements
This research was supported in part by contracts from the Defense Advanced Research Projects Agency under the Integrated Crisis Early Warning System (ICEWS) program (Prime Contract #FA8650-07-C-7749: Lockheed-Martin Advance Technology Laboratories) as well as grants from the National Science Foundation (SES-0096086, SES-0455158, SES-0527564, SES-1004414) and by a Fulbright-Hays Research Fellowship for work by Schrodt at the Peace Research Institute, Oslo (http://www.prio.no). The results and findings in no way represent the views of Lockheed-Martin, the Department of Defense, DARPA, or NSF. It has benefitted from extended discussions and experimentation within the ICEWS team and the KEDS research group at the University of Kansas; we would note in particular contributions from Steve Shellman, Hans Leonard, Brandon Stewart, Jennifer Lautenschlager, Andrew Shilliday, Will Lowe, Steve Purpura, Vladimir Petroff, Baris Kesgin and Matthias Heilke.
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Schrodt, P.A., Van Brackle, D. (2013). Automated Coding of Political Event Data. In: Subrahmanian, V. (eds) Handbook of Computational Approaches to Counterterrorism. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5311-6_2
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