Learn More
Micro-blogging is increasingly extending its role from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, extracting useful information from micro-blogging sites poses signi cant challenges due to the volume of the tra c and the presence of extensive(More)
RFID technologies are being recently adopted in the retail space tracking consumer in-store movements. The RFID-collected data are location sensitive and constantly updated as a consumer moves inside a store. By capturing the entire shopping process including the movement path rather than analyzing merely the shopping basket at check-out, the RFID-collected(More)
Modeling and detecting bursts in data streams is an important area of research with a wide range of applications. In this paper, we present a novel method to analyze and identify correlated burst patterns by considering multiple data streams that co-evolve over time. The main technical contribution of our research is the use of a dynamic probabilistic(More)
Studying information di usion through social networks has become an active research topic with important implications in viral marketing applications. One of the fundamental algorithmic problems related to viral marketing is the In uence Maximization (IM) problem: given an social network, which set of nodes should be considered by the viral marketer as the(More)
Many machine learning, statistical, and computational linguistic methods have been developed to identify sentiment of sentences in documents, yielding promising results. However, most of state-of-the-art methods focus on individual sentences and ignore the impact of context on the meaning of a sentence. In this paper, we propose a method based on(More)
Collaborative tagging systems (CTS) offer an interesting social computing application context for topic detection and tracking research. In this paper, we apply a statistical approach for discovering topic-specific bursts from a popular CTS del.icio.us. This approach allows trend discovery from different components of the system such as users, tags, and(More)
Online social communities have become an important communication channel for people to share and discover information. Pieces of information spread within the community via the underlying social network, from one individual to another. However, with the unprecedented ease and low cost of communication provided by online systems, information overload emerges(More)