Author profiling is a challenging task that consists of identifying a person’s relevant attributes based on the content he/she generates. In this article, we validate a classification method based on the reconstructive classification to identify two demographic attributes, age and gender, for users of social networks based on the text content they publish. For the problem, we consider balanced and unbalanced data, where a gender or age group has a larger presence than the others. The proposed method is based on using the reconstructive property of singular value decomposition (SVD), and its suitability to work with sparse data, to find a matrix with the main latent components to represent the information of specific gender or age classes. Afterward, we use such a matrix to project and reconstruct new users’ information to identify their demographic variables. We test our method with a set of datasets in several languages collected from Twitter and Pinterest users, and we use different evaluation metrics to compare their performance with the ones of several popular classifiers based on traditional machine learning, and on deep learning, and with some relevant works in the literature. The results show that the proposed classifier performs generally well in identifying the age and gender of users in social networks.

In this work we implement and evaluate a methodology to classify multi-labeled web documents into large-scale taxonomies, using their text content. Multi-label hierarchical classification using large-scale taxonomies is a hard task due to problems of scarcity of training data in many nodes of the hierarchy, overlapping of content and complex decision surfaces. We propose a novel feature extraction model called Multilayered Class Discrimination (MCD), which reduces the dimensions of the text-content features of the web documents along the different levels of the hierarchy, helping to discriminate each class from other classes in the same level and reducing the effects of the mentioned problems. The results of categorizing web documents from the DMOZ directory show that our model improves the accuracy of the categorization when compared with the use of word features, and that the results are competitive with the ones presented in the Second LSHTC Challenge.

Hidden salting in digital media involves the intentional addition or distortion of content patterns with the purpose of content filtering. We propose a method to detect portions of a digital text source which are invisible to the end user, when they are rendered on a visual medium (like a computer monitor). The method consists of “tapping” into the rendering process and analyzing the rendering commands to identify portions of the source text (plaintext) which will be invisible for a human reader, using criteria based on text character and background colors, font size, overlapping characters, etc. Moreover, text deemed visible (covertext) is reconstructed from rendering commands and then the character reading order is identified, which could differ from the rendering order. The detection and resolution of hidden salting is evaluated on two e-mail corpora, and the effectiveness of this method in spam filtering task is assessed. We provide a solution to a relevant open problem in content filtering applications, namely the presence of tricks aimed at circumventing automatic filters.

Current surveys from modern astronomical observatories contain a huge amount of data; in particular, the Sloan Digital Sky Survey (SDSS) has reached the order of terabytes of data in images and spectra. Such an amount of information needs to be exploited by sophisticated algorithms that automatically analyze the data in order to extract useful knowledge from the mega databases. In this work we employ evolution strategies (ES) to automatically extract a set of physical parameters corresponding to stellar population synthesis (ages, metallicities, reddening and relative contributions) from a sample of galaxy spectra taken from SDSS. Such parameters are useful in cosmological studies and for understanding galaxy formation, composition, and evolution. We pose this parameter extraction as an optimization problem and then solve it using ES. The idea is to reconstruct each galaxy spectrum from the sample by means of a linear combination of three similar theoretical models, each contributing in a different way to the stellar population synthesis. This linear combination produces a model spectrum that is compared with the original spectrum using a simple difference function. The goal is to find a model that minimizes this difference, using ES as the algorithm to explore the parameter space. We present experimental results using a set of 100 spectra from SDSS Data Release 2 that show that ES are very well suited to extract stellar population parameters from galaxy spectra.

In this work we employ Evolution Strategies (ES) to automatically extract a set of physical parameters (ages, metallicities, reddening and contributions) from a sample of galaxy spectra taken from Sloan Digital Sky Survey (SDSS) for stellar populations studies. We pose this parameter extraction as an optimization problem and then solve it using ES. The idea is to reconstruct each galactic spectrum from the sample by means of a linear combination of three different theoretical models of stellar population synthesis. This combination produces a model spectrum that is compared with the original spectrum using a difference function. The goal is to find a model that minimizes this difference, using ES as the algorithm to explore the parameter space.