reuters_terror — Reuters terror news word network (2001)
- Description
Co-occurrence network of the words (terms) in the news stories released by the news agency Reuters during the 66 consecutive days after the September 11 attacks on the USA, starting at 9:00 AM EST on 9/11/2001. The networks were produced by S. Corman and K. Dooley (Arizona State University) using centering resonance analysis (CRA), and converted to Pajek format by V. Batagelj. Nodes are words, and there is an edge between two words if they appear in the same text unit (sentence) on a given day; each edge corresponds to one day, and is weighted by the number of co-occurrences on that day. The network was used as a case network for the visualization session at the Sunbelt XXII International Social Network Conference (2002). The data were distributed in Pajek format on the Pajek datasets page of V. Batagelj and A. Mrvar, from which this version was obtained.
The network has the following properties:
- Nodes:
name, the word. - Edges:
weight, the number of co-occurrences of the two words on the day;day, the day (1 to 66, where day 1 is September 11, 2001).
- Nodes:
- Tags
- Informational Language Weighted Timestamps Metadata
- Citation
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- S. R. Corman, T. Kuhn, R. D. McPhee, K. J. Dooley, "Studying Complex Discursive Systems: Centering Resonance Analysis of Communication." Human Communication Research 28(2), 157-206 (2002) 10.1111/j.1468-2958.2002.tb00802.x Sci-Hub
- V. Batagelj, A. Mrvar, "Density based approaches to network analysis: Analysis of Reuters terror news network." Workshop on Link Analysis for Detecting Complex Behavior (LinkKDD 2003), Washington, D.C. (2003) www.cs.cmu.edu
- Upstream URL OK
- https://web.archive.org/web/20070104013103/http://vlado.fmf.uni-lj.si/pub/networks/data/CRA/terror.htm
- Networks
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Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties