Hopeful NLP @ LT - EDI - EACL 2021: Finding Hope in Y ou T ube Comment Section (2021)

Abstract: The proliferation of Hate Speech and misinformation in social media is fast becoming a menace to society. In compliment, the dissemination of hate-diffusing, promising and anti-oppressive messages become a unique alternative. Unfortunately, due to its complex nature as well as the relatively limited manifestation in comparison to hostile and neutral content, the identification of Hope Speech becomes a challenge. This work revolves around the detection of Hope Speech in Youtube comments, for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion. We achieve an f-score of 0.93, ranking 1st on the leaderboard for English comments.

## introduction with the rampant adoption of social media, problems like hostile speech detection have caught extensive attention in natural language processing (nlp) research @xcite @xcite . however, there has been little work on the identification of text that promotes positivity and social well-being. as social media becomes predominant in the daily lives of people, it is crucial, not only to protect users from hateful and discriminative content but also encourage communication that triggers optimism and hope. such expression in a narrow sense may be referred to as hope speech and its identification in the digital space as hope speech detection @xcite . however, such a task is further challenging as the definition is highly subjective and evolving. neutral or positive content with no indication of hostility is not necessarily a sufficient determinant. advocacy of ideas that promote social well-being, ethics, equality, inclusion, tolerance, diversity, a fair representation of minorities, or either the appreciation or motivation for an individual or a group, are a few indicators of hope speech. moreover, criticism of oppressive or malicious elements of society may also fall under such a category. furthermore, one does not need to express such beliefs with the present but may incorporate past or future developments. ## related work hope speech detection is a nascent research task by @xcite . the authors proposed automatic identification of positive web content that may diffuse hostility on social media platforms due to political tensions around the 2019 pulwama terror attack 1 . the authors mined a multilingual (hindi and english) corpus by scrapping comments from youtube videos related to the crisis. the authors developed a comprehensive system that took statistical nlp features: n-grams, along with temporal sentiment scores. the system also employed language identification using polyglot fasttext @xcite , to achieve an f1-score of 78.51 and 95.48 auc. however, the work differs from hopeedi (chakravarthi, 2020) as it focuses on alleviation of tension and violence and ignores other aspects of hope. the study focuses on hopeedi dataset as part of the shared task on hope speech detection for equality, diversity, and inclusion. we compare our experimental outcomes with the results with (chakravarthi, 2020) as the baseline. chakravarthi (2020) employed tfidf: token frequency-inverse document frequency along with classifiers such as multinomial naive bayes, k-nearest neighbours, support vector machine, decision tree and logistic regression. decision tree delivered the highest f-score for english and malayalam while tamil performed well with logistic regression. a more thorough comparison occurs in section 4. ## experiments the hopeedi dataset consists of 3 languages: english, tamil and malayalam. the task can be addressed as a sequence classification problem with 3 class: hope speech, not hope speech and not belonging to the given language. weighted f1 score is employed as an evaluation metric over the 3 languages separately. moreover, tamil and malayalam consist of text samples in romanized and native scripts. this section describes the experiments conducted for the task and states their outcomes over the validation set. ## conclusion in this work, we present a simple means of hope speech identification using pre-trained transformers and paraphrasing generation for data augmentation. our future work shall concentrate on interpretability, specifically answering questions like what makes a text an instance of hope speech. moreover, we will attempt to couple more modalities with text, such as audio recording and images or even video clips that collectively promote hope speech.