Cultural Diffusion and Trends in Facebook Photographs

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, that is, photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs. We perform analysis both at macroscopic and microscopic levels, to gain novel insights about global and local visual trends as well as the dynamics of interpersonal cultural exchange and diffusion among Facebook friends. We processed images by automatically classifying the visual content by a convolutional neural network (CNN). Through various statistical tests, we find that socially tied individuals more likely post images showing similar cultural lifestyles. To further identify the main cause of the observed social correlation, we use the Shuffle test and the Preference-based Matched Estimation (PME) test to distinguish the effects of influence and homophily. The results indicate that the visual content of each user's photographs are temporally, although not necessarily causally, correlated with the photographs of their friends, which may suggest the effect of influence. Our paper demonstrates that Facebook photographs exhibit diverse cultural lifestyles and preferences and that the social interaction mediated through the visual channel in social media can be an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper

La ciencia ficción en el mundo árabe: aproximación a sus posibles orígenes, panorama general y futuro del género

Conferencias y Comunicaciones del primer Congreso Internacional de literatura fantástica y ciencia ficción, celebrado del 6 al 9 de mayo de 2008 en la Universidad Carlos III de Madri

Analysis of footwork diagrams from Libro de las grandezas de la espada

The goal of this analysis is to search for a plausible explanation of the rules followed by Pacheco in Libro de las grandezas de la espada to construct the footwork theory explained in it. For this purpose, we are going to geometrically analyse the diagrams presented in the treatise, we are studying it in the order the concepts are explained in the treatise: a presentation of a rigid explanation of the footwork and an apparently low-consistent application of it through the footwork diagrams. Thus, we will compile the data presenting some hypotheses that appear along the way until we can rearrange it to see the pattern that gives us a plausible construction rule for the footwork diagrams. In order to obtain a rule consistent with later Verdadera Destreza treatises and theory, and therefore more plausible as all of them claimed to follow Pacheco’s teachings, we will present a brief analysis of several treatises Common Circle descriptions to see how the conclusions reached match with them. Finally, we are proposing a rule set that Pacheco may have used and an application of it to reconstruct some diagrams of the treatise

Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information

Dimensionality reduction and manifold learning methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) are routinely used to map high-dimensional data into a 2-dimensional space to visualize and explore the data. However, two dimensions are typically insufficient to capture all structure in the data, the salient structure is often already known, and it is not obvious how to extract the remaining information in a similarly effective manner. To fill this gap, we introduce \emph{conditional t-SNE} (ct-SNE), a generalization of t-SNE that discounts prior information from the embedding in the form of labels. To achieve this, we propose a conditioned version of the t-SNE objective, obtaining a single, integrated, and elegant method. ct-SNE has one extra parameter over t-SNE; we investigate its effects and show how to efficiently optimize the objective. Factoring out prior knowledge allows complementary structure to be captured in the embedding, providing new insights. Qualitative and quantitative empirical results on synthetic and (large) real data show ct-SNE is effective and achieves its goal