IIT DHANBAD CODECHAMPS at S em E val-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification (2022)

Abstract: With the growth of the internet, the use of social media based on images has drastically increased like Twitter, Instagram, etc. In these social media, women have a very high contribution as of 75% women use social media multiple times compared to men which is only 65% of men uses social media multiple times a day. However, with this much contribution, it also increases systematic inequality and discrimination offline is replicated in online spaces in the form of MEMEs. A meme is essentially an image characterized by pictorial content with an overlaying text a posteriori introduced by humans, with the main goal of being funny and/or ironic. Although most of them are created with the intent of making funny jokes, in a short time people started to use them as a form of hate and prejudice against women, landing to sexist and aggressive messages in online environments that subsequently amplify the sexual stereotyping and gender inequality of the offline world. This leads to the need for automatic detection of Misogyny MEMEs. Specifically, I described the model submitted for the shared task on Multimedia Automatic Misogyny Identification (MAMI) and my team name is IIT DHANBAD CODECHAMPS.

## introduction with the growth of the internet, social media becomes a crucial part of everyone's life. as every coin has two side positive and negative, social media also comes with a number of problems. the challenge of identifying misogyny @xcite in different social media specially in forms of meme which contains both image and text is very complicated. misogyny meme highly affected the life of women's as its spread hate and prejudice behaviour against women's. social media like twitter, instagram, etc have handled by their own ways. however, detecting such memes is highly challenging. due to this challenge, it attracts the researcher's attention. according to one social media instagram, more than 1 million users shared memes daily. so, with this huge amount of data in social medias and internet it is impossible to detect every misogyny meme by man power. so, we need machine learning, deep learning and artificial intelligence techniques to detect automatically misogyny memes in social media. in this paper, we have explored various machine learning (ml) and deep learning (dl) algorithms for misogyny identification in shared task mami @xcite challenge and my team's name is iit dhanbad codechamps. as per requirement of mami, i have submitted 4 runs for subtask-a. my best run in subtask-a has achieved macro-f1 score of 0.656. ## related works many works related to automatic detection of misogyny, hate, sexism on social media and web have been proposed. abir rahali @xcite proposed a approach for automatic misogyny detection in social media using attention based bidirectional lstm. endang wahyu pamungkas @xcite proposed a method for automatic identification of misogyny in english and italian tweets at evalita 2018 with a multilingual hate lexicon. mario anzovino, elisabetta fersini @xcite proposed a method for automatic identification and classification of misogynistic language on twitter. the main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigation on nlp features and ml models for detecting and classifying misogynistic language. rachael fulper @xcite proposed a relation between misogynistic language in twitter and sexual violence. in their paper they consider all 50 states in washington dc. lakes goenaga, aitziber @xcite atutxa proposed a automatic misogyny identification using neural networks. in this paper they focus on recurrent neural network (rnn) approach using a bidirectional long short term memory (bi-lstm). ## task and dataset description here we have described the dataset and task provided by multimedia automatic misogyny identification (mami @xcite ) challenge. multimedia automatic misogyny identification (mami) task is divided into two sub task. sub-task a: a basic task about misogynous meme identification, where a meme should be categorized either as misogynous or not misogynous (shown in sub-task b: an advanced task, where the type of misogyny should be recognized among potential overlapping categories such as stereotype, shaming, objectification and violence. e.g. here, 1026.jpg represent meme file name . next column contains 5 numbers of zeros and ones . first numbers represent wheather meme is misogynous . second numbers represent wheather meme is shaming . third numbers represent wheather meme is stereotype .fourth numbers represent wheather meme is objectification .fifth numbers represent wheather meme is violence . next col-umn represent text transcription of the meme. ## result and discussions the results of subtask-a are represented in terms of macro-f1 (shown in