Fil Menczer is one of the organizers of Hypertext 2009, the 20th ACM Conference on Hypertext an Hypermedia. The conference will be held June 29-July 1 at the Villa Gualino Convention Centre, on the hills overlooking Torino, Italy. Hypertext is the main venue for high quality peer-reviewed research on “linking.” The Web, the Semantic Web, the Web 2.0, and Social Networks are all manifestations of the success of the link. With a 70% increase in submissions, Hypertext 2009 will have a strong and diverse technical program covering all research concerning links: their semantics, their presentation, the applications, as well as the knowledge that can be derived from their analysis and their effects on society. The conference will also feature demos, posters, a student research competition, four workshops, and keynotes by Lada Adamic and Ricardo Baeza-Yates.
Alessandro Flammini and Filippo Menczer, along with M. Ángeles Serrano from the University of Barcelona, have authored a paper entitled “Modeling Statistical Properties of Written Text” that has been published in the PLoS One. The paper introduces and validates a generative model that explains from simple rules the simultaneous emergence of patterns of written text observed in many languages. The paper focuses on the well-known Zipf’s law of word frequencies, as well as additional patterns such as Heaps’ law of word diversity, the bursty nature of rare words, and similarity among documents. Through their model, the researchers found a connection between word burstiness and the topicality of text. In addition, they identify dynamic word ranking and memory across documents as key mechanisms to explain the organization of written text. The semantic similarity between topics, which is one of the features that the model aims to explain, is visualized by the Similarity Cloud, an online tool developed by computer science graduate student Mark Meiss. The model developed by the researchers and the findings of this paper could lead to improved techniques for identifying key terms that capture the topics of a Web page, which is crucial for matching search queries to relevant results and ads. More…
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NaN is a research group exploring the modeling, simulation, and analysis of complex social and information networks, and the human and artificial agents who live in these networks. Broadly speaking our research spans network science, data science, web science, and computational social science. Recently our focus has been on modeling the dynamic processes that occur online (how information networks grow and evolve, how memes go viral, how social media can be manipulated for the spread of misinformation, how attention bursts and other traffic patterns emerge, etc.) and on the design of tools to make the Web and social media ‘better’ (more trustworthy, reliable, intelligent, autonomous, robust, personalized, contextual, scalable, adaptive, and so on). We collaborate with colleagues at the IU Network Science Institute (IUNI), ISI Foundation, Yahoo Research, and many other institutions.
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