Automatic Misogyny Detection in Social Media: a Survey

This article presents a survey of automated misogyny identification techniques in social media, especially in Twitter. This problem is urgent because of the high speed at which messages on social platforms grow and the widespread use of offensive language (including misogynistic language) in them. In this article we survey approaches proposed in the literature to solve the problem of misogynistic message recognition. These include classical machine learning models like Sup-port Vector Machine, Naive Bayes, Logistic Regression and ensembles of different classical machine learning models and deep neural networks such as Long Short-term memory and Convolutional Neural Networks. We consider results of experiments with these models in different languages: English, Spanish and Italian tweets. The survey describes some features which help to identify misogynistic tweets and some challenges which aim was to create misogyny language classifiers. The survey includes not only models which help to identify misogyny language, but also systems which help to recognize a target of an offense (an individual or a group of persons)

Misogyny Detection and Classification in English Tweets: The Experience of the ITT Team

The problem of online misogyny and women-based offending has become increasingly widespread, and the automatic detection of such messages is an urgent priority. In this paper, we present an approach based on an ensemble of Logistic Regression, Support Vector Machines, and Naïve Bayes models for the detection of misogyny in texts extracted from the Twitter platform. Our method has been presented in the framework of the participation in the Automatic Misogyny Identification (AMI) Shared Task in the EVALITA 2018 evaluation campaign

Automatic Identification of Misogyny in English and Italian Tweets at EVALITA 2018 with a Multilingual Hate Lexicon

In this paper we describe our submission to the shared task of Automatic Misogyny Identification in English and Italian Tweets (AMI) organized at EVALITA 2018. Our approach is based on SVM classifiers and enhanced by stylistic and lexical features. Additionally, we analyze the use of the novel HurtLex multilingual linguistic resource, developed by enriching in a computational and multilingual perspective of the hate words Italian lexicon by the linguist Tullio De Mauro, in order to investigate its impact in this task.Nel presente lavoro descriviamo il sistema inviato allo shared task di Automatic Misogyny Identification (AMI) ad EVALITA 2018. Il nostro approccio si basa su classificatori SVM, ottimizzati da feature stilistiche e lessicali. Inoltre, analizziamo il ruolo della nuova risorsa linguistica HurtLex, un’estensione in prospettiva computazionale e multilingue del lessico di parole per ferire in italiano proposto dal linguista Tullio De Mauro, per meglio comprendere il suo impatto in questo tipo di task

Hurtlex: A Multilingual Lexicon of Words to Hurt

We describe the creation of HurtLex, a multilingual lexicon of hate words. The starting point is the Italian hate lexicon developed by the linguist Tullio De Mauro, organized in 17 categories. It has been expanded through the link to available synset-based computational lexical resources such as MultiWordNet and BabelNet, and evolved in a multi-lingual perspective by semi-automatic translation and expert annotation. A twofold evaluation of HurtLex as a resource for hate speech detection in social media is provided: a qualitative evaluation against an Italian annotated Twitter corpus of hate against immigrants, and an extrinsic evaluation in the context of the AMI@Ibereval2018 shared task, where the resource was exploited for extracting domain-specific lexicon-based features for the supervised classification of misogyny in English and Spanish tweets.L’articolo descrive lo sviluppo di Hurtlex, un lessico multilingue di parole per ferire. Il punto di partenza è il lessico di parole d’odio italiane sviluppato dal linguista Tullio De Mauro, organizzato in 17 categorie. Il lessico è stato espanso sfruttando risorse lessicali sviluppate dalla comunità di Linguistica Computazionale come MultiWordNet e BabelNet e le sue controparti in altre lingue sono state generate semi-automaticamente con traduzione ed annotazione manuale di esperti. Viene presentata sia un’analisi qualitativa della nuova risorsa, mediante l’analisi di corpus di tweet italiani annotati per odio nei confronti dei migranti e una valutazione estrinseca, mediante l’uso della risorsa nell’ambito dello sviluppo di un sistema Automatic Misogyny Identification in tweet in spagnolo ed inglese

Automated Identification of Sexual Orientation and Gender Identity Discriminatory Texts from Issue Comments

In an industry dominated by straight men, many developers representing other gender identities and sexual orientations often encounter hateful or discriminatory messages. Such communications pose barriers to participation for women and LGBTQ+ persons. Due to sheer volume, manual inspection of all communications for discriminatory communication is infeasible for a large-scale Free Open-Source Software (FLOSS) community. To address this challenge, this study aims to develop an automated mechanism to identify Sexual orientation and Gender identity Discriminatory (SGID) texts from software developers' communications. On this goal, we trained and evaluated SGID4SE ( Sexual orientation and Gender Identity Discriminatory text identification for (4) Software Engineering texts) as a supervised learning-based SGID detection tool. SGID4SE incorporates six preprocessing steps and ten state-of-the-art algorithms. SGID4SE implements six different strategies to improve the performance of the minority class. We empirically evaluated each strategy and identified an optimum configuration for each algorithm. In our ten-fold cross-validation-based evaluations, a BERT-based model boosts the best performance with 85.9% precision, 80.0% recall, and 82.9% F1-Score for the SGID class. This model achieves 95.7% accuracy and 80.4% Matthews Correlation Coefficient. Our dataset and tool establish a foundation for further research in this direction

AMI @ EVALITA2020: Automatic Misogyny Identification

Automatic Misogyny Identification (AMI) is a shared task proposed at the Evalita 2020 evaluation campaign. The AMI challenge, based on Italian tweets, is organized into two subtasks: (1) Subtask A about misogyny and aggressiveness identification and (2) Subtask B about the fairness of the model. At the end of the evaluation phase, we received a total of 20 runs for Subtask A and 11 runs for Subtask B, submitted by 8 teams. In this paper, we present an overview of the AMI shared task, the datasets, the evaluation methodology, the results obtained by the participants and a discussion about the methodology adopted by the teams. Finally, we draw some conclusions and discuss future work.Automatic Misogyny Identification (AMI) é uno shared task proposto nella campagna di valutazione Evalita 2020. La challenge AMI, basata su tweet italiani, si distingue in due sub-tasks: (1) subtask A che ha come obiettivo l’identificazione di testi misogini e aggressivi (2) subtask B relativo alla fairness del modello. Al termine della fase di valutazione, sono state ricevute un totale di 20 submissions per il subtask A e 11 per il subtask B, inviate da un totale di 8 team. Presentiamo di seguito una sintesi dello shared task AMI, i dataset, la metodologia di valutazione, i risultati ottenuti dai partecipanti e una discussione sulle metodologie adottate dai diversi team. Infine, vengono discusse le conclusioni e delineati gli sviluppi futuri