TY - JOUR
T1 - A system for ranking organizations using social scale analysis
AU - Tikves, Sukru
AU - Banerjee, Sujogya
AU - Temkit, Hamy
AU - Gokalp, Sedat
AU - Davulcu, Hasan
AU - Sen, Arunabha
AU - Corman, Steven
AU - Woodward, Mark
AU - Nair, Shreejay
AU - Rohmaniyah, Inayah
AU - Amin, Ali
N1 - Funding Information:
This research was supported by US DoDs Minerva Research Initiative Grant N00014-09-1-0815, Project leader: Prof. Mark Woodward, Arizona State University, and the project title is “Finding Allies for the War of Words: Mapping the Diffusion and Influence of Counter-Radical Muslim Discourse”.
Publisher Copyright:
© 2012, Springer-Verlag.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - In this paper, we utilize feature extraction and model-fitting techniques to process the rhetoric found in the web sites of 23 Indonesian Islamic religious organizations to profile their ideology and activity patterns along a hypothesized radical/counter-radical scale, and present an end-to-end system that is able to help researchers to visualize the data in an interactive fashion on a timeline. The subject data of this study is 37,000 articles downloaded from the web sites of these organizations dating from 2001 to 2011. We develop algorithms to rank these organizations by assigning them to probable positions on the scale. We show that the developed Rasch model fits the data using Andersen’s LR-test. We create a gold standard of the ranking of these organizations through an expertise elicitation tool. We compute expert-to-expert agreements, and we present experimental results comparing the performance of three baseline methods to show that the Rasch model not only outperforms the baseline methods, but it is also the only system that performs at expert-level accuracy.
AB - In this paper, we utilize feature extraction and model-fitting techniques to process the rhetoric found in the web sites of 23 Indonesian Islamic religious organizations to profile their ideology and activity patterns along a hypothesized radical/counter-radical scale, and present an end-to-end system that is able to help researchers to visualize the data in an interactive fashion on a timeline. The subject data of this study is 37,000 articles downloaded from the web sites of these organizations dating from 2001 to 2011. We develop algorithms to rank these organizations by assigning them to probable positions on the scale. We show that the developed Rasch model fits the data using Andersen’s LR-test. We create a gold standard of the ranking of these organizations through an expertise elicitation tool. We compute expert-to-expert agreements, and we present experimental results comparing the performance of three baseline methods to show that the Rasch model not only outperforms the baseline methods, but it is also the only system that performs at expert-level accuracy.
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U2 - 10.1007/s13278-012-0072-x
DO - 10.1007/s13278-012-0072-x
M3 - Article
AN - SCOPUS:84947282068
SN - 1869-5450
VL - 3
SP - 313
EP - 328
JO - Social Network Analysis and Mining
JF - Social Network Analysis and Mining
IS - 3
ER -