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Release: September 13, 2019 / Release: October 18, 2019 / Release: October 21, 2019 / Release: October 26, 2019

Social media is a concrete sensor that can be used to measure social momentum. However, hyperbole unfiltered messages posted on social media at this time are a social case, especially if they contain hate speech directed at an exclusive individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the negative effects these messages have on individuals and people. In this document, we present Haternet, a smart platform currently used by the Spanish State Office Against Hate Crimes by the Spanish Foreign Minister, which identifies and monitors the evolution of hate speech on Twitter. The contributions of this research are various. (1) Introducing the first intelligent platform to monitor and visualize hate speech and hate speech on social networks using social network analytics technology. (2) Provide a collection of Spanish hate community data from 6,000 professionally tagged tweets. (3) Comparing different classification methods based on different document representation tactics and text classification styles. (4) The best method consists of coming from a combination Ltsm+Mlp neural network that takes tf-idf rich tweet words, emoticons, and self-actualization tokens as inputs to gain a place under the curve (Auc). 0.828 in the data set, exceeding the previous method reported in the literature.

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hate crimes; sentiment analysis; text classification; predictive policing; social network analysis; Twitter hate crimes; sentiment analysis; text classification; predictive policing; social network analysis; Twitter

The availability of the Internet has dramatically changed our perception of the global world. No child comes from the World Wide Site is social media (SM), which comes in various forms such as online gaming systems, dating services, institutions, online information services, and social networks. Different social networks serve different purposes: sharing ideas (Twitter or Facebook), generating business contacts (Linkedin), sharing photos (Instagram), streaming videos (Youtube), dating (Meetic), and re-abundance. But they all have one tendency. It’s about connecting people. The energy of social media is so great that by 2021, the number of users worldwide is expected to reach 3.02 billion monthly active social media users. That would be one third of the global population.

Among the available social networks, Twitter is currently one of the leading systems and one of the most important sources of information for researchers. Twitter is a real-time social microblogging network where info is regularly posted in front of formal news outlets. Featuring a short message limit (Currently 280 features) and an unfiltered feed, this feature is already growing rapidly, especially at events, along with an average of 500 million tweets posted per day.

In recent years, social media (especially Twitter) has been used to spread hate messages. Hate speech is any type of language that offends an individual or group on the basis of race, ethnicity, sexual orientation, gender, disability, religion, political affiliation, or opinion. The United Nations Discounted Action Plan [1], which adds guidance on disparities between freedom of expression and hate speech, recommends distinguishing between three styles of self-actualization. Expressions that do not lead to criminal charges but are capable of justifying civil charges or administrative penalties are expressions of concern for tolerance, courtesy, and respect for the rights of others without criminal, civil, or administrative penalties.

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In this case, hate crimes are crimes that violate the law because they pretend to be victims. This occurs when the villain selects victims based on the fact that they are included in the exclusive group defined by the above characters. Evidence is available that hate crimes were influenced by one widely known incident [2] (terrorist aggression, uncontrolled migration, protests, riots, etc.). These moments often act as triggers and their effects are magnified in Sm. This makes SM a concrete censor [3] and a valuable news source for crime prediction [4]. Moreover, social networks are full of messages from people inciting sanctions for various target groups. When these messages are collected over the period following the moment of incitement, they can be used to analyze hate crimes across terms of threat development, stabilization, persistence, and mitigation. Therefore, SM monitoring is a priority in the prediction, detection, and analysis of hate crimes.

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Responding to this need, the primary purpose of this document is to design a Haternet platform that can identify and classify hate speech on Twitter, as well as monitor and analyze trends in hate and other negative sentiments. The platform can be used to detect the instigators of hate waves, especially in minorities and individuals belonging to those groups. This adds valuable news to security agencies and police, especially when predicting future crimes or taking follow-up action. In fact, Haternet was developed by the Spanish Ministry of the Interior, more specifically the Office of National Security (Ses) from the Spanish State Service on Hate (Snoahc-Ses). However, the methodology described in this document is not country and language dependent and can be localized. Due to the context of its application, the platform presented in this study only examines the first two types of representation defined in the Discount Action Plan (Which can trigger demands and sanctions). Third, more subjective research is left as a task for the future.

The first module discusses the sampling and classification of tweets and the second module provides tools for analyzing hate speech content on social networks.

Therefore, there are two main contributions to studying the classification of hate speech. (1) A new collection of public data that can be used to test, train, and compare newly developed methods. This contribution is too relevant because so far the author has found three databases of public hate speech [5, 6, 7]. The first two sets of data consist of the ID of the tweet to be downloaded, which is not relevant. Twitter periodically deletes more than one Tweet. In cases that are too militant, the original dataset shown against [5, 6] cannot be retrieved. Also, most of the literature on hate has been compared with them [8, 9, 10, 11]. For this reason, providing a new and independent collection of public knowledge is too significant for the development of this domain in the future. (2) A new class of methods called Double Deep Learning Neural Method, which consists of a combination of Long Short Term Memory (Ltsm) and Multilayer Perceptron (Mlp) neural networks that use words, emoticons, and self-actualization as input. tf-idf Enter enhanced Tweet token. Embeddings are obtained using the neural network based word2vec algorithm. Our experiments show that this method outperforms other types of certificates in the literature.

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The primary findings in this article are available in the second module, Social Network Analyzer. Moreover, the hate literature is only interested in reviewing and suggesting ways to classify hate speech as a repository. Taking it one step further, the classifications provided by Haternet can be used and visualized to impact a network of concepts and actors based on their interactions with hate messages. To the author’s knowledge, this is the first platform along with the properties reported in the literature [12]. have

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