Abstract:
This paper presents a cyber-archaeology approach to social movement research. The approach overcomes many of the issues of scale and complexity facing social research in the Internet, enabling broad and longitudinal study of the virtual communities supporting social movements. Cultural cyber-artifacts of significance to the social movement are collected and classified using automated techniques, enabling analysis across multiple related virtual communities. Approaches to the analysis of cyber-artifacts are guided by perspectives of social movement theory. A case study on a broad group of related social movement virtual communities is presented to demonstrate the efficacy of the framework, and provide a detailed instantiation of the proposed approach for evaluation. © 2010 International Communication Association.
Abstract:
This resGarch presorts preliminary results generated from the semantic retrieval research component of the Illinois Digital Library Initiative (DLI) project. Using a variation of the automatic thesaurus generation techniques, to which we refer as the concept space approach, we aimed to create graphs of domain-specific concepts (terms) and their weighted co-occurrence relationships for all major engineering domains. Merging these concept spaces and providing traversal paths across different concept spaces could potentially help alleviate the vocabulary (difference) problem evident in large-scale information retrieval. We have experimented previously with such a technique for a smaller molecular biology domain (Worm Community System, with 10+ MBs of document collection) with encouraging results. In order to address Ihe scalability issue related to large-scale information retrieval and analysis for the current Illinois DLI project, we recently conducted experiments using the concept space approach on parallel supercomputers. Our test collection included 2+ GBs of computer science and electrical engineering abstracts extracted from the INSPEC database. The concept space approach called for extensive textual and statistical analysis (a form of knowledge discovery) based on automatic indexing and cooccurrence analysis algorithms, both previously tested in the biology domain. Initial testing results using a 512-node CM-5 and a 16processor SGI Power Challenge were promising. Power Challenge was later selected to create a comprehensive computer engineering concept space of about 270,000 terms and 4,000,000+ links using 24.5 hours of CPU time. Our system evaluation involving 12 knowledgeable subjects revealed that the automatically-created computer engineering concept space generated significantly higher concept recall than the human-generated INSPEC computer engineering thesaurus. However, the INSPEC was more precise than the automatic concept space. Our current work mainly involves creating concept spaces for other major engineering domains and developing robust graph matching and traversal algorithms for cross-domain, concept-based retrieval. Future work also will include generating individualized concept spaces for assisting user-specific concept-based information retrieval. © 1996 IEEE.
Abstract:
We experimentally evaluated the Dark Web Forum Portal by focusing on user task performance, usability, cognitive processing requirements, and societal benefits. Our results show that the portal performs perform well when compared with a benchmark forum. © 2012 IEEE.
Abstract:
Recommender systems can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is collaborative filtering, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms. © 2005 ACM.
