Abstract. In this paper, we use signed bipartite graphs to model opinions expressed by one type of entities (e.g., individuals, organizations) about another (e.g., political issues, religious beliefs), and based on the strength of that opinion, partition both types of entities into two clusters. The clustering is done in such a way that support for the second type of entity by the first within a cluster is high and across the cluster is low. We develop an automated partitioning tool that can be used to classify individuals and/or organizations into two disjoint groups based on their beliefs, practices and expressed opinions.
In this paper we utilize feature extraction and model fitting techniques to process the rhetoric found in the web sites of 23 Indonesian religious organizations -comprising a total of 37,000 articles dating from 2005 to 2011 -to profile their ideology and activity patterns along a hypothesized radical/counter-radical scale. We 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 different baseline methods to show that the Rasch model not only outperforms our baseline methods, but it is also the only system that performs at expert-level accuracy.
Abstract-In this paper we utilize feature extraction and model fitting techniques to process the rhetoric found in the web sites of 23 Indonesian religious organizations -comprising a total of 37,000 articles dating from 2005 to 2011 -to profile their ideology and activity patterns along a hypothesized radical/counter-radical scale. We 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 different baseline methods to show that the Rasch model not only outperforms our baseline methods, but it is also the only system that performs at expert-level accuracy.
Abstract-Online social network community now provides an enormous volume of data for analyzing human sentiment about people, places, events and political activities. It is increasingly clear that analysis of such data can provide great insights on the social, political and cultural aspect of the participants of these networks. As part of the Minerva project, currently underway at Arizona State University, we have analyzed a large volume of Twitter data to understand radical political activity in the provinces of Indonesia. Based on analysis of radical/counter radical sentiments expressed in tweets by Twitter users, we create a Heat Map of Indonesia which visually demonstrates the degree of radical activities in various provinces of Indonesia. We create the Heat Map of Indonesia by computing (i) the Radicalization Index and (ii) the Location Index of each Twitter user from Indonesia, who has expressed some radical sentiment in her tweets. The conclusions derived from our analysis matches significantly with the analysis of Wahid Institute, a leading political think tank of Indonesia, thus validating our results.
Abstract-In this study, we aim to obtain "natural groupings" of 151 local non-government organizations and institutions mentioned in a news archive of 77,000 articles spanning a decade (May 1999 to Jan 2010) from Indonesia. One of our goals is to enhance our understanding of counter-radical movements in critical locations in the Muslim world. We present information extraction techniques to recognize entities, and their beliefs and practices in text as a step towards identifying socially significant scales with explanatory power. Then, we proceed to cluster organizations based on these scales. We present experimental results, and discuss challenges in reasoning with the complex interactions of many simultaneous beliefs, practices and attitudes held by the leaders and followers of various organizations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.