Automatic offensive language detection from Twitter data using machine learning and feature selection of metadata

The popularity of social networks has only increased in recent years. In theory, the use of social media was proposed so we could share our views online, keep in contact with loved ones or share good moments of life. However, the reality is not so perfect, so you have people sharing hate speech-related messages, or using it to bully specific individuals, for instance, or even creating robots where their only goal is to target specific situations or people. Identifying who wrote such text is not easy and there are several possible ways of doing it, such as using natural language processing or machine learning algorithms that can investigate and perform predictions using the metadata associated with it. In this work, we present an initial investigation of which are the best machine learning techniques to detect offensive language in tweets. After an analysis of the current trend in the literature about the recent text classification techniques, we have selected Linear SVM and Naive Bayes algorithms for our initial tests. For the preprocessing of data, we have used different techniques for attribute selection that will be justified in the literature section. After our experiments, we have obtained 92% of accuracy and 95% of recall to detect offensive language with Naive Bayes and 90% of accuracy and 92% of recall with Linear SVM. From our understanding, these results overcome our related literature and are a good indicative of the importance of the data description approach we have used

Asymmetrical bloch branes and the hierarchy problem

We investigate a two scalar fields split braneworld model which leads to a possible approach to the hierarchy problem within the thick brane scenario. The model exhibits a resulting asymmetric warp factor suitable for this purpose. The solution is obtained by means of the orbit equation approach for a specific value of one of the parameters. Besides, we analyze the model qualitative behaviour for arbitrary parameters by inspecting the underlying dynamical system defined by the equations which give rise to the braneworld model. We finalize commenting on the metric fluctuation and stability issues.Comment: 8 pages, 7 figure

Information-Entropic for Travelling Solitons in Lorentz and CPT Breaking Systems

In this work we group three research topics apparently disconnected, namely solitons, Lorentz symmetry breaking and entropy. Following a recent work [Phys. Lett. B 713 (2012) 304], we show that it is possible to construct in the context of travelling wave solutions a configurational entropy measure in functional space, from the field configurations. Thus, we investigate the existence and properties of travelling solitons in Lorentz and CPT breaking scenarios for a class of models with two interacting scalar fields. Here, we obtain a complete set of exact solutions for the model studied which display both double and single-kink configurations. In fact, such models are very important in applications that include Bloch branes, Skyrmions, Yang-Mills, Q-balls, oscillons and various superstring-motivated theories. We find that the so-called Configurational Entropy (CE) for travelling solitons, which we name as travelling Configurational Entropy (TCE), shows that the best value of parameter responsible to break the Lorentz symmetry is one where the energy density is distributed equally around the origin. In this way, the information-theoretical measure of travelling solitons in Lorentz symmetry violation scenarios opens a new window to probe situations where the parameters responsible for breaking the symmetries are random. In this case, the TCE selects the best value

New Algorithms for Computing a Single Component of the Discrete Fourier Transform

This paper introduces the theory and hardware implementation of two new algorithms for computing a single component of the discrete Fourier transform. In terms of multiplicative complexity, both algorithms are more efficient, in general, than the well known Goertzel Algorithm.Comment: 4 pages, 3 figures, 1 table. In: 10th International Symposium on Communication Theory and Applications, Ambleside, U

D-Oscillons in the Standard Model-Extension

In this work we investigate the consequences of the Lorentz symmetry violation on extremely long-living, time-dependent, and spatially localized field configurations, named oscillons. This is accomplished in ($D+1$) dimensions for two interacting scalar field theories in the so-called Standard Model-Extension context. We show that $D$-dimensional scalar field lumps can present a typical size $R_{\min }\ll R_{KK}$, where $R_{KK}$ is the associated length scale of extra dimensions in Kaluza-Klein theories. Here, the size $R_{\min }$ is shown to strongly depend on the terms that control the Lorentz violation of the theory. This implies either contraction or dilation of the average radius $R_{\min}$, and a new rule for its composition, likewise. Moreover, we show that the spatial dimensions for existence of oscillating lumps have an upper limit, opening new possibilities to probe the existence of a $D$ -dimensional oscillons at TeV energy scale. Moreover, in a cosmological scenario with Lorentz symmetry breaking, we argue that in the early Universe with an extremely high energy density and a strong Lorentz violation, the typical size $R_{\min }$ was highly dilated. With the expansion and subsequent cooling of the Universe, we propose that it passed through a phase transition towards a Lorentz symmetry, wherein $R_{\min }$ tends to be compact.Comment: 8 pages, final version to appear in PR