Abstract:
With the widespread availability of Business Big Data and the recent advancement in text and Web mining, tremendous opportunities exist for computational and finance researchers to advance research relating to smart market and money. This T&C Department includes three article on smart market and money from distinguished experts in information systems and business. Each article presents unique perspectives, advanced computational methods, and selected results and examples. © 2006 IEEE.
Abstract:
In recent years terrorist groups have been using the World-Wide Web to spread their ideologies, disseminate propaganda, and recruit members. Studying the terrorist Web sites may help us understand the characteristics of these Web sites and predict terrorist activities. In this chapter, we propose to apply network topological analysis methods on systematically collected the terrorist Web site data and to study the structural characteristics at the Web page level. We conducted a case study using the methods on three collections of terrorist Web sites: Middle-Eastern, US domestic, and Latin-American. We found that the Web page networks from these three collections have the small-world and scale-free characteristics. We also found that smaller size Web sites which share similar interests tend to make stronger inter-site linkages, which help them form the giant component in the networks. © 2008 Springer-Verlag Berlin Heidelberg.
Abstract:
Biomedical research is critical to biodefense, which is drawing increasing attention from governments globally as well as from various research communities. The U.S. government has been closely monitoring and regulating biomedical research activities, particularly those studying or involving bioterrorism agents or diseases. Effective surveillance requires comprehensive understanding of extant biomedical research and timely detection of new developments or emerging trends. The rapid knowledge expansion, technical breakthroughs, and spiraling collaboration networks demand greater support for literature search and sharing, which cannot be effectively supported by conventional literature search mechanisms or systems. In this study, we propose an integrated approach that integrates advanced techniques for content analysis, network analysis, and information visualization. We design and implement Arizona Literature Mapper, a Web-based portal that allows users to gain timely, comprehensive understanding of bioterrorism research, including leading scientists, research groups, institutions as well as insights about current mainstream interests or emerging trends. We conduct two user studies to evaluate Arizona Literature Mapper and include a well-known system for benchmarking purposes. According to our results, Arizona Literature Mapper is significantly more effective for supporting users' search of bioterrorism publications than PubMed. Users consider Arizona Literature Mapper more useful and easier to use than PubMed. Users are also more satisfied with Arizona Literature Mapper and show stronger intentions to use it in the future. Assessments of Arizona Literature Mapper's analysis functions are also positive, as our subjects consider them useful, easy to use, and satisfactory. Our results have important implications that are also discussed in the article.
Abstract:
Web 2.0 has brought a huge amount of user-generated, social media data that contains rich information about people's opinions and ideas towards various products, services, and ongoing social and political events. Nowadays, many companies start to look into and try to leverage this new type of data to understand their customers in order to make better business strategies and services. As a nation with rapid economic growth in recently years, China has become visible and started to play an important role in the global business and economy. Also, with the large number of Chinese Internet users, a considerable amount of options about Chinese business and market have been expressed in social media sites. Thus, it will be of interest to explore and understand those user-generated contents in Chinese. In this study, we develop an integrated framework to analyze user sentiments from Chinese social media sites by leveraging sentiment analysis techniques. Based on the framework, we conduct experiments on two popular Chinese Web forums, both related to business and marketing. By utilizing Elastic Net together with a rich body of feature representations, we achieve the highest F-measures of 84.4 and 86.7 % for the two data sets, respectively. We also demonstrate the interpretability of Elastic Net by discussing the top-ranked features with positive or negative sentiments. © 2013 Springer Science+Business Media New York.
Abstract:
Criminals have been using the Internet to distribute a wide range of illegal materials globally in an anonymous manner, making criminal identity tracing difficult in the cybercrime investigation process. In this study we propose to adopt the authorship analysis framework to automatically trace identities of cyber criminals through messages they post on the Internet. Under this framework, three types of message features, including style markers, structural features, and content-specific features, are extracted and inductive learning algorithms are used to build feature-based models to identify authorship of illegal messages. To evaluate the effectiveness of this framework, we conducted an experimental study on data sets of English and Chinese email and online newsgroup messages. We experimented with all three types of message features and three inductive learning algorithms. The results indicate that the proposed approach can discover real identities of authors of both English and Chinese Internet messages with relatively high accuracies. © Springer-Verlag Berlin Heidelberg 2003.
