IIT Dhanbad @ LT - EDI - ACL 2022- Hope Speech Detection for Equality, Diversity, and Inclusion (2022)

Abstract: Hope is considered significant for the wellbeing,recuperation and restoration of humanlife by health professionals. Hope speech reflectsthe belief that one can discover pathwaysto their desired objectives and become rousedto utilise those pathways. Hope speech offerssupport, reassurance, suggestions, inspirationand insight. Hate speech is a prevalent practicethat society has to struggle with everyday. The freedom of speech and ease of anonymitygranted by social media has also resulted inincitement to hatred. In this paper, we workto identify and promote positive and supportivecontent on these platforms. We work withseveral machine learning models to classify socialmedia comments as hope speech or nonhopespeech in English. This paper portraysour work for the Shared Task on Hope SpeechDetection for Equality, Diversity, and Inclusionat LT-EDI-ACL 2022.

## introduction nowadays, social media has become a significant part of our lives and just like everything it has its pros and cons. various benefits of social media come with several challenges including hate speech, offensive and profane content getting published targeting an individual, a group or a society. social media has become a breeding ground for hate speech and cyberbullying @xcite . offensive content in online socialization have seriously affected daily life of people @xcite @xcite . social media companies such as, youtube, facebook, and twitter have their own approaches to eliminate the hate speech content or anything which negatively affects the society. however, detecting such objectionable content at the earliest to curb the menace of spreading such news online is still a major challenge faced by social media companies and researchers @xcite . it is very essential to detect such behaviour. the amount of data generated on social media sites can be estimated from the fact that, every second,on average, around 6,000 tweets are generated @xcite @xcite @xcite . content moderation of such a huge data is difficult to achieve exclusively through man power. social networking sites are struggling with content moderation @xcite @xcite @xcite . our work aims to change the prevalent way of thinking by moving away from a preoccupation with discrimination, loneliness or the worst things in life to building the confidence, support and good qualities based on comments by individuals. in this paper, we have explored several machine learning models for classification of social media comments as hope speech or non-hope speech in english. ## related works several works have been proposed to detect hope speech across social platforms. @xcite proposed a work with several transformerbased models to classify social media comments as hope speech or not hope speech in english, malayalam and tamil languages. @xcite have used the transformer-based pretrained models along with the customized versions of those models for detecting hope and not hope speech for equality, diversity and inclusion in dravidian languages . @xcite experimented with two approaches. they used contextual embeddings to train classifiers using logistic regression, random forest, svm, and lstm based models.they also used a majority voting ensemble of 11 models which were obtained by fine-tuning pre-trained transformer models (bert, albert, roberta, indicbert) after adding an output layer. ## task and dataset description here we have described the dataset and task provided by hope speech detection for equality, diversity, and inclusion challenge. this is a comment / post level classification task.in this, youtube comments are given and the systems submitted by us should classify it into 'hope speech' and 'not hope speech'. (shown in ## conclusions and future work we have completed the task using various classification algorithms and evaluated the performance of different classification algorithms for hope speech detection for equality, diversity, and inclusion shared task. our overall best score is 0.6130. we look forward to experimenting with different advance algorithm or neural network models. we are also looking forward to work on random multi model classification algorithm for better accuracy and classification. also, fine tuning the parameters of the algorithm can help in improvement of the overall performance. we shall be exploring these tasks in the coming days.