Abstract:
The convenience of the Internet has made it possible for activist groups to easily form alliances through their websites to appeal to wider audience and increase their impact. In this study, we investigate the potential of using Social Network Analysis (SNA) and Writeprints to discover the fusion of activitst ideas on the Internet, focusing on the Falun Gong movement. We find that network visualization is very useful to reveal how different types of websites or ideas are associated and, in some cases, mixed together. Furthermore, the measures of centrality in SNA help to reveal which websites most prominently link to other websites. We find that Writeprints can be used to identify the ideas which an author gradually introduces and combines through a series of messages. ©2008 IEEE.
Abstract:
The rapid development of Web 2.0 allows people to be involved in online interactions more easily than before and facilitates the formation of virtual communities. Online communities exert influence on their members' online and offline behaviors. Therefore, they are of increasing interest to researchers and business managers. Most virtual community studies consider subjects in the same Web application belong to one community. This boundary-defining method neglects subtle opinion differences among participants with similar interests. It is necessary to unveil the community structure of online participants to overcome this limitation. Previous community detection studies usually account for the structural factor of social networks to build their models. Based on the affect theory of social exchange, this research argues that emotions involved in social interactions should be considered in the community detection process. We propose a framework to extract social interactions and interaction emotions from user-generated contents and a GN-H co-training algorithm to utilize the two types of information in community detection. We show the benefit of including emotion information in community detection using simulated data. We also conduct a case study on a real-world Web forum dataset to exemplify the utility of the framework in identifying communities to support further analysis.
Abstract:
Criminals often falsify their identities intentionally in order to deter police investigations. In this paper we focus on uncovering patterns of criminal identity deception observed through a case study performed at a local law enforcement agency. We define criminal identity deception based on an understanding of the various theories of deception. We interview a police detective expert and discuss the characteristics of criminal identity deception. A taxonomy for criminal identity deception was built to represent the different patterns that were identified in the case study. We also discuss methods currently employed by law enforcement agencies to detect deception. Police database systems contain little information that can help reveal deceptive identities. Thus, in order to identify deception, police officers rely mainly on investigation. Current methods for detecting deceptive criminal identities are neither effective nor efficient. Therefore we propose an automated solution to help solve this problem.
Abstract:
Accounting numbers such as earnings per share are an important information source that conveys the value of firms. Previous studies on the return-earnings relation have confirmed that stock prices react to the information content in accounting numbers. However, other information sources such as financial news may also contain value-relevant information and affect investors' reaction to earnings announcements. We quantify news coverage about S&P 500 companies in the Wall Street Journal (WSJ) before earnings announcements and model its interaction with the return-earnings relation. Our empirical results show that news coverage decreases the information content of unexpected earnings and thus leads to a lower earnings response coefficient (ERC) for firms with higher news coverage. Statistically significant interaction between news coverage and unexpected earnings was observed. News coverage does not impact cumulated abnormal returns directly. We further document that this finding is not driven by firm size. The results suggest that financial news may play an important role in conveying value-related information to the markets. © 2010 Elsevier B.V. All rights reserved.
Abstract:
Neural networks have been used in various applications on the World Wide Web, but most of them only rely on the available input-output examples without incorporating Web-specific knowledge, such as Web link analysis, into the network design. In this paper, we propose a new approach in which the Web is modeled as an asymmetric Hopfield Net. Each neuron in the network represents a Web page, and the connections between neurons represent the hyperlinks between Web pages. Web content analysis and Web link analysis are also incorporated into the model by adding a page content score function and a link score function into the weights of the neurons and the synapses, respectively. A simulation study was conducted to compare the proposed model with traditional Web search algorithms, namely, a breadth-first search and a best-first search using PageRank as the heuristic. The results showed that the proposed model performed more efficiently and effectively in searching for domain-specific Web pages. We believe that the model can also be useful in other Web applications such as Web page clustering and search result ranking. © 2007 IEEE.
