Deep Learning Based Misogynistic Bangla Text Identification from Social Media

Misogyny is characterized by hostility, hatred, aversion, intimidation, and violence against women. With the rise of social media, it has become one of the most convenient platforms for expressing woman-hating speech. As a result, misogyny is gaining appeal and societal standards are being violated. With millions of Bangladeshi Facebook users, misogyny is growing increasingly prevalent in Bangla as well. In this paper, we have proposed automatically identifying misogynistic content in Bangla on social media platforms in order to evaluate the problem's challenges. As there is no existing Bangla dataset for analyzing misogynistic text, we generated our own. We have applied various deep-learning algorithms to improve the classification of misogynistic text categories. LSTM and RNN models are used for designing the model architecture in deep learning. Models are evaluated using the confusion matrix, accuracy, and f1-scores. The results indicate that LSTM outperforms RNN in terms of accuracy by 67 %

Gender inequality on Twitter during the UK election of 2019

Social media platforms such as Twitter play an essential role in politics and social movements nowadays. The aim of this paper is to compare and contrast the language used on Twitter to refer to the candidates of the last UK general election of December 2019 in order to raise awareness of gender inequality in politics. The methodology followed is based on three aspects: (a) a quantitative analysis using Sketch Engine to extract the main collocates from the corpus; (b) a sentiment analysis of the compiled tweets by means of two lexicon classifications: BING (Hu & Liu, 2004) and NRC (Mohammad & Turney, 2013), which classifies words into eight basic emotions and two sentiments (positive and negative); and (c) a qualitative analysis employing a Critical Discourse Analysis approach (Fairclough, 2013) to examine verbal abuse towards women from a linguistics perspective

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

A comparison of machine learning approaches for detecting misogynistic Speech in urban dictionary

—Recent moves to consider misogyny as a hate crime have refocused efforts for owners of web properties to detect and remove misogynistic speech. This paper considers the use of deep learning techniques for detection of misogyny in Urban Dictionary, a crowdsourced online dictionary for slang words and phrases. We compare the performance of two deep learning techniques, Bi-LSTM and Bi-GRU, to detect misogynistic speech with the performance of more conventional machine learning techniques, logistic regression, Naive-Bayes classification, and Random Forest classification. We find that both deep learning techniques examined have greater accuracy in detecting misogyny in the Urban Dictionary than the other techniques examined. Dublin, Ireland [email protected] it was announced that the UK Law Commission would review whether misogynistic conduct should be treated as a hate crime [6]. Index Terms—misogyny, hate speech, recurrent neural networks, deep learning, LSTM, machine learning, urban dictionar

Misogyny Detection in Social Media on the Twitter Platform

The thesis is devoted to the problem of misogyny detection in social media. In the work we analyse the difference between all offensive language and misogyny language in social media, and review the best existing approaches to detect offensive and misogynistic language, which are based on classical machine learning and neural networks. We also review recent shared tasks aimed to detect misogyny in social media, several of which we have participated in. We propose an approach to the detection and classification of misogyny in texts, based on the construction of an ensemble of models of classical machine learning: Logistic Regression, Naive Bayes, Support Vectors Machines. Also, at the preprocessing stage we used some linguistic features, and novel approaches which allow us to improve the quality of classification. We tested the model on the real datasets both English and multilingual corpora. The results we achieved with our model are highly competitive in this area and demonstrate the capability for future improvement

Detecting Hate Speech Against Women in English Tweets

Hate speech is prevalent in social media platforms. Systems that can automatically detect offensive content are of great value to assist human curators with removal of hateful language. In this paper, we present machine learning models developed at UW Tacoma for detection of misogyny, i.e. hate speech against women, in English tweets, and the results obtained with these models in the shared task for Automatic Misogyny Identification (AMI) at EVALITA2018. © 2018 CEUR-WS. All Rights Reserved

Deep learning e contenuti misogini: analisi linguistica dell'errore nei compiti di riconoscimento di misoginia nei meme

L'odio verso le donne è un fenomeno pervasivo della nostra cultura, e può essere espresso attraverso modi e forme molto diversi fra loro. Uno dei mezzi più recenti sono i meme, che grazie alla popolarità dei social network assumono un'importanza sempre crescente nel veicolare e diffondere la misoginia. Negli ultimi anni si è posta l'attenzione su questo mezzo comunicativo, creando modelli multimodali che fossero in grado di rilevare hate speech nei meme. La tesi propone un'analisi linguistica dell'errore di un modello basato sul deep learning di classificazione di contenuti misogini in un dataset di 10.000 meme, per cercare di capire quali sono le ragioni a livello linguistico che hanno portato il modello a una classificazione errata. Fra le analisi condotte, sono stati usati liste di frequenza, n-grammi, identity term ed embedding, che hanno evidenziato alcune criticità del modello

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)