hate-alert@ LT - EDI -2023: Hope Speech Detection Using Transformer-Based Models (2023)

Abstract: Social media platforms have become integral to our daily lives, facilitating instant sharing of thoughts and ideas. While these platforms often host inspiring, motivational, and positive content, the research community has recognized the significance of such messages by labeling them as “hope speech”. In light of this, we delve into the detection of hope speech on social media platforms. Specifically, we explore various transformer-based model setups for the LT-EDI shared task at RANLP 2023. We observe that the performance of the models varies across languages. Overall, the finetuned m-BERT model showcases the best performance among all the models across languages. Our models secured the first position in Bulgarian and Hindi languages and achieved the third position for the Spanish language in the respective task.

## introduction social media enables people to instantly share their thoughts and connect with billions of other users. however, it has been observed that malicious users sometimes exploit social media to spread harmful speech. consequently, significant efforts have been made to detect and mitigate hate speech. nevertheless, social media is not solely characterized by hateful messages @xcite . people also express their feelings and the mental stress they experience in their lives, which may arise from online harassment or workplace discrimination. analyzing people's posting patterns can provide insights into their mental health @xcite . in contrast, social media posts can also be inspiring, motivational, or offer positive suggestions. for instance, individuals may share stories about overcoming life's stresses, furnishing hope and encouragement to others facing similar challenges. such posts can particularly benefit individuals feeling down or experiencing mental stress. recog-nizing the significance of these positive messages, researchers have started investigating this field, labeling such posts as "hope speech" @xcite . hope is commonly associated with offering promises, support, reassurance, suggestions, or inspiration to individuals during periods of illness, stress, loneliness, and depression (chakravarthi, 2020). psychologists, sociologists, and social workers affiliated with the association of hope have concluded that hope can serve as a valuable tool in preventing suicide or self-harm @xcite . by studying the dynamics of hope speech on social media, researchers aim to understand better its impact and potential in promoting well-being and mental health. this research has the potential to contribute to the development of strategies and interventions that utilize hope speech to support individuals in difficult times, ultimately fostering a more positive and supportive social media environment chakravarthi @xcite . although english is the most commonly used language on social media platforms, people from various linguistic backgrounds participate and share their thoughts in local languages or dialects. as a result, there is a need for a comprehensive understanding across multiple languages. to promote research on hope speech detection, the organizers of the hope speech detection for equality, diversity, and inclusion (lt-edi -ranlp 2023) (kumaresan et al., 2023) foot_0 shared task has introduced the task of detecting hope speech in four languages: bulgarian, english, hindi, and spanish. since our team, hate-alert, is particularly interested in lowresource languages, we participated in the bulgarian, hindi, and spanish languages. this paper presents the methodologies we employed to identify hope speech detection, which led to our team achieving first place for the bulgarian and hindi languages and third place for the spanish language in the overall leaderboard standings of the shared tasks. ## related work this section explores some of the related topics around hope speech detection. ## dataset description the language technology for equality, diversity, and inclusion (lt-edi-2023) shared task at ranlp 2023 focuses on the detection of hope speech in social media through a classification problem. the main objective of this shared task is to develop methodologies for detecting hope speech in multiple languages. ## methodology this section discusses the preprocessing steps and various models that we implement for the detection of hope speech. ## results ## conclusion in this shared task, we deal with a novel challenge of detecting hope speech across multiple languages. to evaluate the performance, we employed transformer-based models such as m-bert and xlmr. our observations revealed that for the bulgarian language, the mbert+ann model configuration achieved the best results. conversely, for hindi and spanish, fine-tuned mbert models exhibited superior performance. in the future, we plan to explore additional transformer-based models, as well as the recent llm (large language model) models, to enhance our approach in this domain further.