BFCAI at C om MA @ ICON 2021: Support Vector Machines for Multilingual Gender Biased and Communal Language Identification (2021)

Abstract: This paper presents the system that has been submitted to the multilingual gender biased and communal language identification shared task by BFCAI team. The proposed model used Support Vector Machines (SVMs) as a classification algorithm. The features have been extracted using TF/IDF model with unigram and bigram. The proposed model is very simple and there are no external resources are needed to build the model.

## introduction the manner in which humans communicate and their communities has been completely changed due to the widespread in social network platforms. the integration of communication and data sharing through platforms like youtube, facebook, and twitter caused the emergence and vocalization of hate between users. the intensity and hostility lying in the written expressions is a matter of grave concern. articulations of hatefulness often cause breaking down or weakening communities. as the impact of such articulation travels from online to offline domain, resultant reactions frequently lead to incidents like organized riot-like situations and unfortunate casualties to ultimately broaden the scope of marginalization of individuals as well as communities @xcite . there exists widespread, simmering distrust, hatred and insult towards specific groups of people. users of social network platforms in most of nations are predisposed both to believe disinformation and to share misinformation about discriminated groups in face-to-face as well as in social network platforms @xcite . in recent times, there have been a large number of studies exploring various aspects of hateful and aggressive language and their computational modelling and automatic detection such as toxic comments, trolling, racism, online aggression, cyberbullying, hate speech, abusive language and offensive language @xcite @xcite @xcite . prior studies have explored the use of aggressive and hateful language on different platforms such as twitter and facebook posts. one of the recent studies was to make use of youtube comments for computational modelling of aggression and misogyny. some of the earlier studies on computational modelling of misogyny have focused almost exclusively on tweets @xcite @xcite . also, all of these studies have focused on either english or european languages like greek @xcite , italian @xcite and spanish @xcite . the use of a wide range of aggressive and hateful content on social media becomes interesting as well as challenging to study in context to india which is a secular nation with religious as well as linguistic and cultural heterogeneity @xcite . the broader aim of research in this area is to understand how communal and sexually threatening misogynistic content is linguistically and structurally constructed. in addition, how this kind of contents evaluated by the other participants in the discourse @xcite . data originating from social media is multi-lingual data, which makes the automatic analysis of social media is incredibly challenging. in addition, people of the multi-language countries such as india always use code-mixed contents. to convey challenges of automatic multilingual abusive harmful content detection, we present the model has been submitted to comma@icon shared task at icon 2021. in this paper, a machine learning based model will be developed to detect the misogyny, gender biased and communal languages on social media. the system will use a supervised text classification model that would be trained using a dataset annotated at two levels with labels pertaining to sexual and communal aggression. in this paper, a demonstration of the submitted system by bfcai team to comma@icon2021 shared task is given. the rest of the paper is organized as follows; related work is outlined in section 2, section 3 presents the task and dataset, methodology and algorithms have been used to develop our system are described in section 4. in section 5, experimental settings and discussion of the results are given. conclusion and future work are given in the last section. ## related work a lot of research works have been done in this area. @xcite provide a way to investigate gender bias observed in well-known word embeddings, e.g., word2vec @xcite . they use a set of binary gender pair to gather a gender subspace. for in-explicitly gendered words, the difficulty of the word embeddings that project onto this subspace can be removed to de-bias the embeddings within the gender direction. they furthermore endorse a softer model that balances reconstruction of the precise embeddings at the same time as minimizing part of the embeddings that project onto the gender subspace. the word embedding association test (weat) was performed by @xcite . it is entirely predicated on the hypothesis. also, it phrases embeddings that are closer collectively in the high dimensional area and are semantically nearer. they find strong evidence of social biases in pre-trained phrase embeddings. @xcite perform experiments on the use of the de-biasing strategies proposed by @xcite and @xcite . they explain that bias elimination approaches primarily based on gender routes are inefficient in getting rid of all factors of bias. in an excessive dimensional space, the spatial distribution of the gender-impartial phrase embeddings stay nearly identical after de-biasing. this permits a gender-impartial classifier to nevertheless select the cues that encode different semantic factors of bias. @xcite create a multilingual european languages dataset for bias evaluation. they recommended numerous approaches for quantifying bias from both intrinsic and extrinsic perspectives. experimental outcomes display that choosing a specific alignment target space and using bert improve performance. they pick out the embeddings aligned to a gender-wealthy language to lessen the unfairness. ## task and dataset the aim of multilingual gender biased and communal language identification (comma@icon) shared task is to identify aggressive, gender biased or communally charged contents in text @xcite . the shared task encompass hindi, meitei, bangla (indian variety) and english. lately, hate speech related research gained a great interest in the area of computational linguistics. the shared task is divided into three sub-tasks (a, b and c) to identify aggressive, gender biased and communal biased contents respectively. ## methodology in this section, we present details of the proposed model and the algorithms used. ## experiments and results the following experimental settings have been used while training our classifiers: stochastic gradient descent (sgd) optimization algorithm has been used for optimizing the parameters of linear classifier. "hinge" function has been used as a loss function for linear classifier and svm uses the linear kernel while training. the activation function has been used while training mlp was logistic function and there exist 20 neurons in the hidden layer. the hard voting technique was used for ensemble approach. we used python programming languages and the library sklearn, which contains an integrated set of functions for machine learning framework. the experiments have been conducted on macbook air device with 8 gb memory, 1.8 ghz intel core i5. ## conclusion in this paper, a machine learning approaches have been used for creating a model for detecting the multilingual gender biased and communal contents. presented model achieved good results compared to its simplicity. extension of our work includes using deep learning models to develop the classifier and test it on much bigger dataset.