Vicente López

Statistical analysis of the social network and discussion threads in slashdot

We analyze the social network emerging from the user comment activity on the website Slashdot. The network presents common features of traditional social networks such as a giant component, small average path length and high clustering, but differs from them showing moderate reciprocity and neutral assortativity by degree. Using Kolmogorov-Smirnov statistical tests, we show that the degree distributions are better explained by log-normal instead of power-law distributions. We also study the structure of discussion threads using an intuitive radial tree representation. Threads show strong heterogeneity and self-similarity throughout the different nesting levels of a conversation. We use these results to propose a simple measure to evaluate the degree of controversy provoked by a post.

Description and Prediction of Slashdot Activity

We perform a statistical analysis of users reaction time to a new discussion thread in online debates on the popular news site Slashdot. First, we show with Kolmogorov-Smirnov tests that a mixture of two log-normal distributions combined with the circadian rhythm of the community is able to explain with surprising accuracy the reaction time of comments within a discussion thread. Second, this characterization allows to predict intermediate and long-term user behavior with acceptable precision. The prediction method is based on activity-prototypes, which consist of a mixture of two log-normal distributions, and represent the average activity in a particular region of the circadian cycle.

CUCWeb: a Catalan corpus built from the web

This paper presents CUCWeb, a 166 million word corpus for Catalan built by crawling the Web. The corpus has been annotated with NLP tools and made available to language users through a flexible web interface. The developed architecture is quite general, so that it can be used to create corpora for other languages.

Phase Transition and Hysteresis in an Ensemble of Stochastic Spiking Neurons

An ensemble of stochastic nonleaky integrate-and-fire neurons with global, delayed, and excitatory coupling and a small refractory period is analyzed. Simulations with adiabatic changes of the coupling strength indicate the presence of a phase transition accompanied by a hysteresis around a critical coupling strength. Below the critical coupling production of spikes in the ensemble is governed by the stochastic dynamics, whereas for coupling greater than the critical value, the stochastic dynamics loses its influence and the units organize into several clusters with self-sustained activity. All units within one cluster spike in unison, and the clusters themselves are phase-locked. Theoretical analysis leads to upper and lower bounds for the average interspike interval of the ensemble valid for all possible coupling strengths. The bounds allow calculating the limit behavior for large ensembles and characterize the phase transition analytically. These results may be extensible to pulse-coupled oscillators.

Event modeling of message interchange in stochastic neural ensembles

We propose a modeling framework based on the event-driven paradigm for populations of neurons which interchange messages. Unlike other strategies our approach is focused on the dynamics at the mesoscopic level (spike production and reception) and does not determine the microstates of the neurons. We apply the technique on a discrete model of stochastic ensembles and on extensions of this model to the continuous time domain. Due to the event-driven nature of the method efficient large-scale simulations can be performed without precision errors. The approach uses spike predictions as evidences and a one-step update of the predictions is performed every time an event occurs, resulting in a more efficient solution than the existing strategies.