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.

The emergence of Cross-Border E-commerce (CBeC) has brought substantial changes to both businesses and consumers. Although CBeC businesses have existed for less than a decade, many academic researchers addressed important issues in this context. It is essential to evaluate what has been studied through a structured review of the literature and derive meaningful insights given that research on this topic is new and largely fragmented. Therefore, this study conducts a review of CBeC literature to find the current gaps and fragmentation to provide guidelines for future research. The review shows that research in this domain needs more attention and enforcement to address the current research gaps. Addressing the current gaps helps academia build a rigorous body of knowledge and enables practitioners to solve challenging business problems.

In this paper the authors proposed a database sampling framework that aims to minimize the time necessary to produce a sample database. They argue that the performance of current relational database sampling techniques that maintain the data integrity of the sample database is low and a faster strategy needs to be devised. The sampling method targets the production environment of a system under development that generally consists of large amounts of data computationally costly to analyze. The results have been improved due to the fact that the authors have selected the users that they had more information about them and they have made the data table denser. Therefore, by increasing the data and making the rating more comprehensive for all the users they can help to produce the more and better association rules. The obtained results were not that much suitable for Jester dataset but with their proposed methods the authors have tried to improve the quantity and quality of the rules. These results indicate that the effectiveness of the system greatly depends on the input data and the applied dataset. In addition, if the user rates more number of the items the system efficiency will be more increased.