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Association: Name: International Studies Association URL: http://www.isanet.org
Citation: URL: http://www.allacademic.com/meta/p98724_index.html Direct Link: HTML Code:

Shellman, Stephen., Stewart, Brandon. and Reeves, Andrew. "Media Generated Data: The Effects of Source Bias on Event Data Analysis" Paper presented at the annual meeting of the International Studies Association, Town & Country Resort and Convention Center, San Diego, California, USA, Mar 22, 2006 <Not Available>. 2009-05-25 <http://www.allacademic.com/meta/p98724_index.html>

Shellman, S. M., Stewart, B. M. and Reeves, A. , 2006-03-22 "Media Generated Data: The Effects of Source Bias on Event Data Analysis" Paper presented at the annual meeting of the International Studies Association, Town & Country Resort and Convention Center, San Diego, California, USA Online <APPLICATION/PDF>. 2009-05-25 from http://www.allacademic.com/meta/p98724_index.html

Publication Type: Conference Paper/Unpublished Manuscript Abstract: In this paper, we examine the effects of source bias on statistical inferences drawn from event data analyses. Most event data projects code actors, targets, events, and dates from the same news report. For example most of the early Kansas Event Data System (KEDS) datasets code only Reuters news reports, while recent KEDS datasets code Agence France Presse (AFP) reports. One of the goals of Project Civil Strife (PCS) ?a new event data project? is to code event data from several news sources and examine the effects that source bias has on the inferences we draw from statistical time-series models. In this study, we concentrate on Cambodia from 1980-2005 using automated content analyzed datasets collected from multiple sources (i.e. BBC, Xinhua, Jiji Press Ticker, Associated Press, and AFP). Provided we draw different inferences from different sources, we combine the different datasets into a single comprehensive dataset and eliminate duplicate events to generate a less biased dataset.

Automated Coding of Sub-state Actors in Event Data

Reconciling Alternative Sources for Event Data: A Statistical Approach

A New Kind of Social Science: Moving Ahead with Results from Reverse Wolfram Models as Applied to Event Data