Abstract:
More and more women are participating in and exchanging opinions through community-based online social media. Questions concerning gender differences in the new media have been raised. This paper proposes a feature-based text classification framework to examine online gender differences between Web forum posters by analyzing writing styles and topics of interest. Our experiment on an Islamic women's political forum shows that feature sets containing both content-free and content-specific features perform significantly better than those consisting of only content-free features, feature selection can improve the classification results significantly, and female and male participants have significantly different topics of interest. © 2011 IEEE.
Abstract:
Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to "intelligent" information retrieval and indexing. More recently, information science researchers have turned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to familiarize readers with the techniques, we present three promising methods: The symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval. An experiment using a 8000-record COMPEN database was performed to examine the performances of these inductive query-by-example techniques in comparison with the performance of the conventional relevance feedback method. The machine learning techniques were shown to be able to help identify new documents which are similar to documents initially suggested by users, and documents which contain similar concepts to each other. Genetic algorithms, in particular, were found to out-perform relevance feedback in both document recall and precision. We believe these inductive machine learning techniques hold promise for the ability to analyze users' preferred documents (or records), identify users' underlying information needs, and also suggest alternatives for search for database management systems and Internet applications.
Abstract:
Although non-English-speaking online populations are growing rapidly, support for searching non-English Web content is much weaker than for English content. Prior research has implicitly assumed English to be the primary language used on the Web, but this is not the case for many non-English-speaking regions. This research proposes a language-independent approach that uses meta-searching, statistical language processing, summarization, categorization, and visualization techniques to build high-quality domain-specific collections and to support searching and browsing of non-English information. Based on this approach, we developed SBizPort and AMedPort for the Spanish business and Arabic medical domains respectively. Experimental results showed that the portals achieved significantly better search accuracy, information quality, and overall satisfaction than benchmark search engines. Subjects strongly favored the portals' search and browse functionality and user interface. This research thus contributes to developing and validating a useful approach to non-English Web searching and providing an example of supporting decision-making in non-English Web domains. © 2006 Elsevier B.V. All rights reserved.
Abstract:
The relationships between phenomena observed in the real world and their representations in virtual contexts have generated interest among researchers. In particular, the manifestations of social movements in virtual environments have been examined, with many studies dedicated to the analysis of the virtual linkages between groups. In this research, a form of link analysis was performed to examine the relationship between virtual linkage intensity and real world physical proximity among the social movement groups identified in the Southern Poverty Law Center Spring 2009 Intelligence Report. Findings indicate the existence of significant relationships between virtual linkage intensity and physical proximity, distinctive to various ideological categorizations. The results provide valuable insights into the behaviors of social movements in virtual environments. © 2010 IEEE.
