Mining Offensive Language on Social Media
Authors: Alessandro Maisto, Serena Pelosi, Simonetta Vietri, Pierluigi Vitale (Università di Salerno, 2017)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
The present research deals with the automatic annotation and classification of vulgar ad offensive speech on social media. In this paper we will test the effectiveness of the computational treatment of the taboo contents shared on the web, the output is a corpus of 31,749 Facebook comments which has been automatically annotated through a lexicon-based method for the automatic identification and classification of taboo expressions.

Introduction
Flaming, trolling, harassment, cyberbullying, cyberstalking, cyberthreats are all terms used for referring to vulgar and offensive contents shared on the web. The shapes can be different and the focus can be on various topics, such as physical appearance, ethnicity, sexuality, social acceptance and so forth. Although taboo language is generally considered to be the strongest clue of harassment in the web, it must be clarified that the presence of bad words in posts does not necessarily indicate the presence of offensive behaviors. The words collected in vulgar lexicons, in some cases, are neutral or even positive. Moreover, profanity can be used with comical or satirical purposes, and bad words are often just the expression of strong emotions AUTHOR. In this paper, we propose a system for the automatic treatment of vulgar and offensive utterances in Italian. The strength of our method is that lexical items are not considered in isolation. Instead, we recognize the power of the local context of the words, which can modulate the meaning of words, phrases and sentences. Section soa briefly illustrates the state of the art contributions on offensive language modeling. Next, Section LGR describes the Italian lexical and grammatical resources for the automatic detection of taboo language in Italian. Then, Section evaluation explains how we tested our method and resources on a Facebook corpus and describes the results of the taboo expressions automatic annotation. Finally, Section conclusion reports the future works that will enhance our research.

Conclusion
In this paper we described an experiment on the detection and classification of offenses, threats and insults shared through User Generated Contents. As a matter of fact, in May 2016, the European Commission, together with companies like Facebook, Twitter, YouTube and Microsoft, underlined the relevance of these topics by presenting a code of conduct which aimed to constrain the virality of illegal online violence and hate speech, with a special focus on utterances fomenting racism, xenophobia and terrorist contents. The negative impact of such practices is not limited to individuals, but strongly affects the freedom of expression and the democratic discourse on the Web. Our research focused on a particular Facebook page, which became famous in Italy for the number of times it has been shut down due to its disturbing content. More than 31,000 users' comments downloaded from this page have been automatically annotated according to a dataset of taboo expressions, in the form of simple words and multiword expressions. This operation has led to a hate speech annotated corpus which distinguishes eight harassment semantic fields, four types of insult and four hate targets (traits). The evaluation of the experiment performances confirmed the hypothesis that the local context of words represents an essential feature for an effective hate speech mining on the web. In future works we will test the interaction of the taboo item located in the corpus with some Italian Contextual Valence Shifters AUTHOR in order to verify if the sentence context of the insult indicators affects the semantic orientation of the items into an Opinion Mining view. Furthermore, it would be interesting to verify the efficacy of our resources and our method on different domains, Political Communication, among others. In the end, just because the automatic extraction has been done in this paper on a very polarized corpus, future analyses will focus on testing the reliability of this research on more neutral collections of texts.