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Application date: September 13, 2019 / Modification date: October 18, 2019 / Pass date: October 21, 2019 / Issue date: October 26, 2019
Social media is a real sensor that can be used to measure social momentum. However, the overwhelming, unfiltered messages posted on social media today are a social problem, especially if those messages contain hate speech directed at specific individuals or groups. In this context, governments and non-governmental organizations (NGOs) are concerned about the negative impact of these messages on individuals and society. In this document, we present HaterNet, an intelligent system currently in use by the Spanish State Office Against Hate Crimes by the Spanish Secretary of State, which identifies and monitors the evolution of hate speech on Twitter. The contributions of this study are multifaceted. (1) Introducing the first intelligent system to monitor and visualize hate speech and hate speech in social networks using social network analysis technology. (2) Provides a Spanish hate community dataset of 6,000 professionally tagged tweets. (3) Compare different classification methods based on different document representation strategies and text classification models. (4) The best method consists of a combination of LTSM+MLP neural networks that take tf-idf-rich tweet words, emoticons, and expression tokens as input to obtain an area under the curve (AUC). 0.828 in the data set, exceeding previous methods reported in the literature.
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Hate crimes; sentiment analysis; text classification; predictive policing; social network analysis; Twitter hate crime; sentiment analysis; text classification; predictive policing; social network analysis; Twitter
The global availability of the Internet has dramatically changed our perception of the world. One of the children of the World Wide Web is social media (SM), which exists in various forms such as online gaming platforms, dating apps, forums, online information services and social networks. Different social networks serve different purposes: sharing ideas (Twitter or Facebook), making business contacts (LinkedIn), sharing photos (Instagram), streaming videos (YouTube), dating (Meetic) and more. But they all have one thing in common. It’s about connecting people. The power 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 a third of the world’s population.
Among existing social networks, Twitter is currently one of the leading platforms and one of the most important sources of information for researchers. Twitter is a real-time social microblogging network where news is posted regularly in front of official news outlets. Featuring a short message limit (currently 280 characters) and an unfiltered feed, this feature has grown rapidly, especially in events, with an average of 500 million tweets posted per day.
In recent years, social media (especially Twitter) has been used to spread hateful 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 UN Rabat Action Plan [1], which provides guidelines on the distinction between freedom of expression and hate speech, recommends distinguishing between three types of expression. Expressions that do not lead to criminal prosecution but which may justify civil claims or administrative sanctions 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 regard, a hate crime is a crime that breaks the law because of prejudice against the victim. This happens when criminals select victims based on the fact that they belong to a particular group defined by the aforementioned traits. There is evidence that hate crimes were influenced by a single widely known incident[2] (terrorist attacks, uncontrolled migration, protests, riots, etc.). These events often act as triggers and their effects are magnified within SM. This makes SM a real sensor [3] and a valuable source of information for crime prediction [4]. In fact, social networks are full of messages from people instigating punishments for various target groups. When these messages are collected over a period of time following a sedition event, they can be used to analyze hate crimes at all stages of threat growth, stabilization, persistence, and reduction. SM’s monitoring therefore becomes a priority in the prediction, detection and analysis of hate crimes.
In response to this need, the main purpose of this document is to design a HaterNet system that can identify and classify hate speech on Twitter, as well as monitor and analyze hate trends and other negative sentiments. The system can be used to detect instigators of hate waves, especially against minorities and individuals belonging to these groups. It provides valuable information to security agencies and the police, especially when predicting future crimes or taking follow-up actions. In fact, HaterNet was jointly developed by the Spanish Ministry of Interior, more specifically the National Security Office (SES) of the Spanish State Service against Hate (SNOAHC-SES). However, the methodology described in this document is country- and language-independent and can be localized. Due to the context of the application, the system presented in this study only examines the first two types of representations defined in the Rabat Action Plan (which may lead to claims and penalties). Third, more subjective research is left as a future task.
The first module deals with sampling and classification of tweets and the second module provides tools for analyzing the content of hate speech in social networks.
Therefore, the contribution to the study of classification of hate speech is essentially twofold. (1) New public data sets that can be used to test, train, and benchmark newly developed methods. This contribution is highly relevant as the authors have so far found three databases of public hate speech [5, 6, 7]. The first two data sets consist of the IDs of the tweets to download, which is irrelevant. Twitter periodically deletes some Tweets. In a particularly aggressive case, the original dataset shown in [5, 6] cannot be retrieved. Also, most of the literature on hate has been compared with them [8, 9, 10, 11]. Therefore, providing a new, independent public data set is very important for the future development of this domain. (2) A new class of methods called Dual Deep Learning Neural Methods, which consists of a combination of Long Short Term Memory (LTSM) and Multilayer Perceptron (MLP) neural networks that take words, emoticons and expressions as inputs. tf-idf Enhanced Tweet token input. The embeddings are obtained using the neural network based word2vec algorithm. Our experiments show that this method outperforms reference models in the literature.
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The main innovation in this article is in the second module, the Social Network Analyzer. In fact, the hate literature is only interested in reviewing and suggesting ways to classify hate speech as a repository. Taking it one step further, the classification provided by HaterNet can be used and visualized to create networks of concepts and actors based on their relationship to hate messages. To the best of the author’s knowledge, this is the first system with properties reported in literature [12]. have
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