A Checkpoint on Multilingual Misogyny Identification (2022)

Abstract: We address the problem of identifying misogyny in tweets in mono and multilingual settings in three languages: English, Italian, and Spanish. We explore model variations considering single and multiple languages both in the pre-training of the transformer and in the training of the downstream taskto explore the feasibility of detecting misogyny through a transfer learning approach across multiple languages. That is, we train monolingual transformers with monolingual data, and multilingual transformers with both monolingual and multilingual data. Our models reach state-of-the-art performance on all three languages. The single-language BERT models perform the best, closely followed by different configurations of multilingual BERT models. The performance drops in zero-shot classification across languages. Our error analysis shows that multilingual and monolingual models tend to make the same mistakes.

## introduction misogynous contents express hate towards women in the form of insulting, sexual harassment, male privilege, patriarchy, gender discrimination, belittling, violence, body shaming and sexual objectification @xcite . according to a study by vox-osservatorio italiano sui diritti on hate speech against minorities (women, homosexuals, migrants, people with disabilities, jews and muslims) in italian tweets, foot_0 women are the most targeted group. they observed a significant increase in the number of misogynous tweets from 2019 to 2021: shifting from 26% to 44% of all hateful posts. @xcite observed a correlation between misogyny on twitter and domestic violence in specific areas has, stressing the importance of flagging such contents to try to dim their impact online. we target the problem of identifying misogyny in multiple languages. this work represents a first step towards investigating the specificity of misogyny with respect to language and culture. to address this novel research question, we test two hypotheses: h1 more data boosts the model performance, even if it is in a different language; therefore, considering training material in diverse languages benefits in the prediction of misogyny in such languages. h2 misogyny is language-specific and therefore a monolingual model performs better, even if it is trained on smaller data. we rely on: (a) data in each of the languages in isolation; or (b) data in various languages in conjunction, through the training of a single multilingual model. 2 we exploit monolingual transformers (bert @xcite ) for three languages -english, italian, and spanish -and one multilingual transformer (multilingual bert @xcite ). we perform a thorough exploration combining different settings, which include training monolingual transformers with monolingual data, multilingual transformers with monolingual data, and multilingual transformers with multilingual data. section 2 summarizes the related work on misogyny identification, both in mono-and multilingual settings. section 3 describes the datasets. section 4 describes the methodology, whereas section 5 discusses the obtained results. section 6 shows our error analysis. section 7 and 8 provide a conclusion and an overview on the societal impact and limitations of our work. ## related work monolingual approaches the increasing number of hateful posts against women has attracted the interest of the scientific community, but it remains an underexplored field compared to other types of hate speech @xcite . work on automatic misogyny identification has been carried out in a limited number of languages. for instance, the automatic misogyny identification (ami) series of shared tasks launched in evalita @xcite and ibereval @xcite has produced evaluation frameworks to identify misogynous tweets in english, italian and spanish. hateval at semeval 2019 @xcite focused on the detection of hate speech towards women and immigrants in english and spanish. participants in those shared tasks mostly used tf-idf representations (e.g., @xcite ), word embeddings (e.g., fabrizi (2020)), and sentence embeddings (e.g., @xcite ). when extracting lexical features from social media, it is common to represent hashtags, emoticons and mentions as well. for instance, pamungkas and patti (2019) considered both a bag of hashtags and a bag of emojis. they also encoded information about the occurrence of swear words. among the most commonly-used classifiers there are recurrent neural networks @xcite , convolutional neural networks (da silva and roman, 2020), shallow models @xcite and transformer-based models @xcite , which perform the best. multilingual approaches few works are focused on the multilingual identification of misogyny. basile and rubagotti (2018) adopted a bleaching approach, i.e. transforming lexical strings into more abstract features @xcite , and tested their model on italian and english. they use an svm with n-gram features. this is a close work to ours: they train on l1 and test on l1, train on l2 and test on l2, and they also train and test on both languages in combination. pamungkas and patti (2019) created bilingual misogynist data in english, italian and spanish with machine translation to train in a source language and predict in a target language with an lstm. we neglect the use of machine translation at all stages. pamungkas et al. (2020) adopted an approach similar to ours, using multilingual transformers to identify english, spanish and italian misogynist tweets. the difference is that they only train a model on one language and test it on a different one, without considering all language combinations. differently from the previous works, we do not focus on model performance or engineering, but we head toward investigating a novel research question: is misogyny language-specific? ## the multi-ami evaluation framework we consider misogyny datasets in three languages, released under two editions of the ami shared task: ami at ibereval 2018 @xcite and ami at evalita 2018 @xcite . ami at ibereval 2018 focused on identifying misogyny on english and spanish tweets, and in classifying misogynistic instances in different categories. ami at evalita 2018 focused on two tasks in italian. task a addressed misogyny identification, whereas task b aimed at recognizing whether a misogynous tweet is person-specific or generally addressed towards a group of women. we address the binary problem alone: whether a tweet is misogynist or not. we stick to the evaluation metric of ami: the f 1 measure. for direct comparison with our models we consider the best-performing approaches in both shared tasks. for italian, @xcite used tf-idf weighting combined with singular value decomposition and an ensemble of classifiers. for english, @xcite concatenated sentence and average word embeddings with tf-idf weights coupled with a logistic regression model. for spanish, pamungkas et al. ( 2018 ) applied an svm with a series of lexical features, including lexicons of abusive words. the bottom row of ## model description our models to identify misogynous tweets are built on different variations of bert @xcite . in the monolingual settings, we use bertbase-uncased for english @xcite , bertbase-spanish-wwm-uncased for spanish @xcite and alberto for italian @xcite . for the multilingual settings, we use multilingual bert (mbert) @xcite . mbert has the same architecture as bert, but it is trained on wikipedia articles in multiple languages @xcite . we also apply mbert in monolingual settings, to observe its behaviour in zero-shot classification across languages. our output layer is a soft-max with two units. we use the categorical cross-entropy loss function and the adamw optimizer with a learning rate of 1-8 @xcite , batch size of 16 and 4 training epochs. ## experiments and results our objective is to assess whether and to what extent considering training material in diverse languages benefits in the prediction of misogyny in multiple languages. we carried out a number of experiments to test hypotheses h1 and h2 (cf. section 1). we head toward investigating the way in which misogyny is expressed in different languages. even if the impact of shared vocabulary in multilingual settings remains unclear @xcite , we explore the feasibility of using multilingual embeddings to produce zero-shot classifications across languages -training on l 1 to predict on l 2 -and as a data augmentation technique -training on l 1 +l 2 to predict on l 1 . we trained ten models considering all combinations of data in english (en), spanish (es) and italian (it): (i) one bert model per language, (ii) one mbert model per language, (iii) one mbert model per each language pair, and (iv) one mbert model with all three languages. the monolingual bert models consistently perform the best, improving over the best ami approaches (cf. section 3). there is a performance drop when monolingual models are trained on top of mbert, with the model trained on english achieving the poorest performance: as low as f 1 =0.14 and 0.17 when tested on spanish and italian and six points lower on english than the monolingual bert alternative. the results suggest that this transfer learning approach is not suitable for languages which are relatively far from each other, e.g., a romance and a germanic one. considering a second language during training improves the predictions of the mbert models (i) on english in all three cases, (ii) on spanish with pair en-es, but (iii) not on italian. indeed, combining english and spanish produces better results for both languages than when combining either with italian. considering all three languages results in mixed effects. it has the best mbert performance on english, but runs short by one point with respect to the pairwise combinations on the other two languages. the best performance on all three languages together is obtained when neglecting the training data in english: f 1 =0.84. these results confirm h1 only partially. on the one hand, monolingual models built on top of a monolingual bert performs the best. on the other hand, considering multilingual training data with a multilingual bert improves over considering monolingual data alone. to 0.54; on italian it does from 0.87 to 0.85. these results play in favour of h2: with the same amount of training data, the models do not benefit from data in other languages. although this hints that h2 is true, these experiments are not enough to prove that misogyny is language-specific. the results obtained with the mbert models when trained on single languages -no difference when compared against bert models on italian, but a drop of six and four points on english and spanishgive more confidence that h2 might be true. ## error analysis we conducted an error analysis to assess how and which kind of errors are transferred from the monoto the multilingual setting. we want to answer two questions. question q1 allows observing the behavior of the multilingual model with respect to the monolingual ones. question q2 helps to identify the words that are most likely responsible for the misclassification in the three languages. q1 which instances are classified differently by the monolingual and the multilingual model? the number of false positives and false negatives behave similarly in all languages. we discuss instances in english for the sake of clarity. we analyse the instances that the monolingual model (bert en) classified correctly and the multilingual one (mbert en-es-it) got wrong. we find 122 instances, with 51 false negative (fn) and 71 false positive (fp). among the fn, the five most common lexical words are hysterical, woman, skank, women and ass. among the fp, the words rape and women are very present, followed by fucking, fuck and shut. we notice that fn instances are more lexically diverse. we also observe the intersection of misogynist tweets between the two models. the mono and q2 which words are most present in instances misclassified by both mono and multilingual models? we first observe instances misclassified by both models. we find 70 fns and 131 fps. for english, the most frequent words are the 1 la ragazza che lavora nel negozio dove vado a fare sempre shopping mi ha detto che ho un bel culo :3333 the girl working at the place where i always do shopping told me i have a nice ass. 2 he said she said di ashley tisdale fa uscire il puttanone che è in me he said she said by ashley tisdale brings out the bitch in me. 3 ciao kikka buon pm quanto 6 figa e sexy @xcite hi kikka good evening you are so hot and sexy 4 figa stai zitta che sono a casa da sola oh don't tell me i'm home alone for spanish, words puta, polla and cállate are common for both settings. we focus the rest of the analysis on italian, since it shows the biggest discrepancies. ## final remarks we explored the contribution of adding multilingual training material in the automatic identification of misogynist tweets in three languages: english, spanish and italian. our models trained on monolingual data achieve state-of-the-art performance. the inclusion of data in one or two other languages impacts the performance negatively when compared to bert models, but positively when compared to mbert models. multilingual models can be used as data augmentation technique -train on l 1 +l 2 to predict on l 1 , but they are not suitable for zero-shot classification across languages -train on l 1 to predict on l 2 , hinting that misogyny might be language-specific, but further experiments are required. ## societal impact and limitations this work represents a starting point toward investigating whether misogyny is language-specific. analysing the differences of misogyny across languages and cultures is important, since it can help policymakers to develop country-specific policies to mitigate its impact. on twitter, as well as on other platforms, interactions can be carried out in different languages. we head toward a real-world application, which considers the multilingualism of the platform. users would benefit from a system able to flag misogynous tweets in multiple languages. such system would raise awareness and ultimately make a more enjoyable online environment for women. among the limitations of this work, we currently focus on three languages only, neglecting geographical information. as a result, not enough attention is paid to culture. moreover, currently our models are not interpretable, and that would be an important aspect to raise awareness in the general public.