The rise in violent crime across major U.S. cities, fueled mainly by gang members using social media to broadcast messages of loss and aggression, poses an urgent challenge. Although prior research has examined gang-affiliated social media content, there remains a crucial gap in identifying which posts serve as credible signals of impending violence. Addressing this gap is essential for enhancing community safety, improving resource allocation, and optimizing law enforcement strategies. This study introduces a novel research model grounded in a contextualized adaptation of signaling theory. The model identifies key indicators of credible signals, such as follower count, specific hashtags, and retweet counts, which correlate with gang-related aggression. Environmental factors, such as temperature, are also examined for their influence on violent crime escalation. Using this contextualized theory, we designed a machine learning model to predict violent crime counts, training it on a dataset of 143,700 gang-affiliated tweets and their accompanying text and metadata. This approach enables automated identification of credible social media signals related to gang violence. The findings contribute to theory and practice by offering new insights into social media credibility and its link to violent crime, and by demonstrating how such signals can be used for prediction. Furthermore, the predictive model provides law enforcement with advanced tools to anticipate crime and inform community-based prevention strategies and policy development.

In spite of affordability, portability, and convenience of mobile handheld devices, there are a number of usability problems associated with Web access through those devices, largely attributable to their inherent physical constraints. In the past decade, there have been increasing empirical studies on interface issues and usability of mobile handheld devices and mobile Web. However, researchers have been frequently using emulators of handheld devices running on desktop computers instead of real, physical handheld devices in those studies. Such a phenomenon raises validity and generalizability concerns given the differences between emulators and physical devices. This research empirically investigates whether the user performance and perception of mobile Web on an emulator is equivalent to or at least consistent with that on a physical device. The findings suggest that an emulator may not be able to emulate user performance and perception with physical handheld devices, and thus should be used with caution.

Globalization has led to the increasing use of organizational teams comprising individuals with diverse cultural backgrounds. Existing research suggests that collaborative software may benefit multicultural teams. However, most prior studies are limited by their focus on U.S. and Western cultures. We explore this issue by comprehensively examining the literature on cultural effects on collaborative software use. This article makes several contributions by providing common nomenclatures and theoretical perspectives that are essential to promoting scientific progress in this area. It focuses mainly on empirical collaborative software studies in which culture is a key conceptual construct. We discuss underlying cultural theories, research methodologies, and findings of major collaborative software studies on the impact of culture. This article provides insights into various issues surrounding this line of research and highlights future research opportunities.

Online deception is disrupting our daily life, organizational process, and even national security. Existing approaches to online deception detection follow a traditional paradigm by using a set of cues as antecedents for deception detection, which may be hindered by ineffective cue identification. Motivated by the strength of statistical language models (SLMs) in capturing the dependency of words in text without explicit feature extraction, we developed SLMs to detect online deception. We also addressed the data sparsity problem in building SLMs in general and in deception detection in specific using smoothing and vocabulary pruning techniques. The developed SLMs were evaluated empirically with diverse datasets. The results showed that the proposed SLM approach to deception detection outperformed a state-of-the-art text categorization method as well as traditional feature-based methods.

Web service composition can enhance the efficiency and agility of knowledge management by composing individual Web services together for complex business requirements. There are two main research streams in knowledge representation for Web service composition: the syntactic-based approach and the semantic-based approach. Despite the promises brought by each approach, the two streams are largely separated from each other. In this article, we propose an integrated ontology-supported Web service composition framework, which provides a novel solution to organizational knowledge management. By synergistically leveraging both syntactic-based and semantic-based approaches, this framework provides dual modes to perform service composition. Ontologies are employed to enrich semantics at both the service description and composition levels. The proposed conceptual framework has been implemented in the corporate financial services domain. It is demonstrated that the shared ontology helps to fulfill automated and on-the-fly service composition in particular and knowledge management in general.

Internet has been universally recognized as a medium for network-enabled transfer of information and knowledge in various areas. E-Learning technology, which delivers educational material electronically via the Internet, has been widely used in both academic education and corporate training. However, existing e-Learning systems have various limitations, such as presenting multimedia instructional content in static, passive, and unstructured manners and giving learners insufficient control over learning content and process. As a result, higher effectiveness and greater societal potential of e-Learning are hindered. In this paper, based on the constructivist learning theory, we propose a new concept called Virtual Mentor (VM) that refers to a multimedia-integrated e-Learning environment that emphasizes interactivity, personalization, and intelligence. A prototype VM system that integrates state-of-the-art information technologies will be introduced. Results from preliminary studies have demonstrated that learning through a Virtual Mentor environment can be as effective as, or even more effective than, traditional classroom learning. Our research gears toward developing more appealing and effective e-Learning technology.

Abstract The role of information retrieval (IR) in support of decision making and knowledge management has become increasingly significant. Confronted by various problems in traditional keyword‐based IR, many researchers have been investigating the potential of natural language processing (NLP) technologies. Despite widespread application of NLP in IR and high expectations that NLP can address the problems of traditional IR, research and development of an NLP component for an IR system still lacks support and guidance from a cohesive framework. In this paper, we propose a theoretical framework called NLPIR that aims at integrating NLP into IR and at generalizing broad application of NLP in IR. Some existing NLP techniques are described to validate the framework, which not only can be applied to current research, but is also envisioned to support future research and development in IR that involve NLP.