Information cascades are ubiquitous in both physical society and online social media, taking on large variations in structures, dynamics and semantics. Although there has been much progress on understanding the dynamics and semantics of information cascades, little is known about their structural patterns. In this paper, we explore a large-scale dataset including 432 million information cascade...

The SNAP information extraction system has been developed as a part of a three-year SNAP projec t sponsored by the National Science Foundation. The main goal of the SNAP project is to build a massively parallel computer capable of fast and accurate natural language processing [5] . Throughout the project, a parallel computer was built in the Parallel Knowledge Processing Laboratory at USC, and ...

Attitudes can be seen as object evaluations stored in memory. Accordingly, attitude structure may be seen as a memory structure with dynamic implications for information processing. In this article, an associative network model for the representation of stored attitudes was assumed, and 2 manifestations of the notion of spreading activation within the confines of such a model were examined. Stu...

Most of the existing document and web search engines rely on keyword-based queries. To find matches, these queries are processed using retrieval algorithms that rely on word frequencies, topic recentness, document authority, and (in some cases) available ontologies. In this paper, we propose an innovative approach to exploring text collections using a novel keywords-by-concepts (KbC) graph, whi...

SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT architecture) has been developed to simulate users as they perform unfamiliar information-seeking tasks on the World Wide Web (WWW). SNIF-ACT selects actions based on the measure of information scent, which is calculated by a spreading activation mechanism that captures the mutual relevance of the contents of a WWW page to the...

Keyword-matching systems based on simple models of semantic relatedness are inadequate at modelling the ambiguities in natural language text, and cannot reliably address the increasingly complex information needs of users. In this paper we propose novel methods for computing semantic relatedness by spreading activation energy over the hyperlink structure of Wikipedia. We demonstrate that our te...

Ontologies and spreading activation are known terms within the scope of information retrieval. In this paper we introduce SPREADR, an integrated adaptation mechanism for web applications that uses ontologies for representing the application domain as well as context information like location, user history and local time. Those context factors can be modeled in an ontology and be linked to certa...

Within the context of a three year applied research project conducted from 2003-2006 in a North American university library, staff were encouraged to reconsider organizational assumptions and design processes. The project involved an organizational leader and an external consultant who introduced and collaboratively applied Soft Systems Methodology (SSM) practice. Project results suggest the ef...

Information of interest to users is often distributed over a set of documents. Users can specify their request for information as a query/topic – a set of one or more sentences or questions. Producing a good summary of the relevant information relies on understanding the query and linking it with the associated set of documents. To “understand” the query we expand it using encyclopedic knowledg...

The burst in the use of online social networks over the last decade has provided evidence that current rumor spreading models miss some fundamental ingredients in order to reproduce how information is disseminated. In particular, recent literature has revealed that these models fail to reproduce the fact that some nodes in a network have an influential role when it comes to spread a piece of in...