T ech SSN at S em E val-2022 Task 5: Multimedia Automatic Misogyny Identification using Deep Learning Models (2022)

Abstract: Research is progressing in a fast manner in the field of offensive, hate speech, abusive and sarcastic data. Tackling hate speech against women is urgent and really needed to give respect to the lady of our life. This paper describes the system used for identifying misogynous content using images and text. The system developed by the team TECHSSN uses transformer models to detect the misogynous content from text and Convolutional Neural Network model for image data. Various models like BERT, ALBERT, XLNET and CNN are explored and the combination of ALBERT and CNN as an ensemble model provides better results than the rest. This system was developed for the task 5 of the competition, SemEval 2022.

## introduction in our society, women are facing lot of challenges in terms of education, employment, career and life. eventhough women are better off now-a-days, there are not considered as equal to men in many situations. with the invent and rapid usage of social media platforms, offensive images and texts conveying several forms of hate against women are transmitted and spread online in a fast manner. although opportunities for women have been increased on the internet, systematic inequality and discrimination offline is continued in online in the form of offensive contents through memes. a meme is essentially an image characterized by a pictorial content with an overlaying text introduced by human on it. most of the memes are created mainly for funniness, still it is used for ironic purpose too. the task 5 by @xcite in semeval 2022 mainly focused on identifying the misogynous memes using textual and image contents. there are two subtasks in this. subtask a is to categorize the memes as misogynous or not misogynous. subtask b is to classify the misogynous memes into one of the categories like stereotype, shaming, objectification and violence. ## related work the survey on various techniques used for automatic misogyny identification (ami) tasks happened in evalita 2018 and iberaleval 2018 were discussed in detail by @xcite . machine learning (svm, naive bayes, logistic regression) and deep learning techniques (cnn, gru, rnn, lstm) are used and achieved an accuracy of 90% in iberaleval task and 70% in evalita task. garcía-díaz et al. ( 2021 ) uses sentiment analysis and social computing technologies with word embeddings and linguistic features for ami that achieves better results than traditional machine learning algorithms. hate speech against women is handled by @xcite . pamungkas et al. (2020) created models using rnn and bert for multilingual misogyny detection. we have performed irony and offensive language detection in earlier semeval workshop tasks @xcite al. (2021), sivanaiah et al. (2020), sivanaiah et al. (2019) and sivanaiah et al. (2018 ## results and discussions we have used variants of bert models for analyzing text data and cnn for image data. the results are tabulated in in subtask a, we have used albert, xlnet and basic bert techniques to classify the given sample as misogynous or non-misogynous using the text data. albert model gives better results than other bert variants. cnn model is used for the classification of image data. the combined ensemble model (albert + cnn) gives better f1 score than using them separately. for the subtask b, we have worked on two models bert and ensemble model. ensemble model with multi-label classification model in transformers and cnn is used for predicting the subcategories. the ensemble model (image and text) achieved better score than the bert model (text alone) and the performance can be increased by tweaking the hyper parameters. ## conclusion user generated content in social media is rapidly increasing day by day that detecting and limiting the diffusion of sarcastic and hate speech content against women is tedious. automatic identification and removal of these contents is the current topic of research. many shared tasks are conducted in various conferences for automatic misogyny identification (ami). task 5 in semeval 2022 focuses on multimedia automatic misogyny identification (mami) with the help of image and text data. transformer models are used to identify the misogynous content from the text and cnn model is used to detect from the image content. performance of the system can be increased by tweeking the parameters in the transformer models. the current system trains the text and visual modalities independently on the labels. however, memes portray misogynistic content though a combination of text and image cues, and training these models independently might miss out on the context provided by the other modality, which might be crucial to further improve the performance of this system.