KEC AI DSNLP @ LT - EDI -2024:Caste and Migration Hate Speech Detection using Machine Learning Techniques (2024)

Abstract: Commonly used language defines “hate speech” as objectionable statements that may jeopardize societal harmony by singling out a group or a person based on fundamental traits (including gender, caste, or religion). Using machine learning techniques, our research focuses on identifying hate speech in social media comments. Using a variety of machine learning methods, we created machine learning models to detect hate speech. An approximate Macro F1 of 0.60 was attained by the created models.

## introduction caste and migration hate speech refer to the use of discriminatory language or expressions that target individuals or groups based on their caste or migration status @xcite . these forms of hate speech can manifest in various ways, including verbal abuse, written messages, online content, or even physical actions @xcite . the effects of caste and migration hate speech can be profound and detrimental on both individual and societal levels. hate speech can cause significant psychological distress and emotional harm to individuals who are targeted @xcite . it can lead to feelings of fear, anxiety, depression, and a sense of isolation. hate speech contributes to the division and polarization of communities. it can create or exacerbate existing tensions between different caste or migrant groups, leading to social fragmentation @xcite . caste and migration hate speech can have a significant impact on social media due to the widespread reach and instantaneous nature of online platforms @xcite . detecting and addressing hate speech in social media comments is important for maintaining a safe and inclusive online environment @xcite . for example, pakistan is a country where caste still persists. caste-based hate speech is severe and pervasive in pakistan's countryside @xcite . social media users accused dalits of spreading the covid-19 pandemic because they are dirty and consume dead animals during the lockdown days. activities related to caste discrimination also occur in india. detecting hate speech in online comments can be challenging due to several factors, and these difficulties pose significant obstacles for both automated systems and human moderators @xcite . hate speech often relies on context, cultural nuances, and sarcasm. automated systems may struggle to understand these subtleties, leading to false positives or negatives @xcite . the same words or phrases may have different meanings in different contexts. hate speech detection becomes more complex in multilingual and multicultural environments @xcite . different languages, dialects, and cultural norms contribute to variations in expression, making it challenging for automated systems to cover a diverse range of content accurately @xcite . in this paper we mainly work on tamil-english social media comments. the shared task on "caste and migration hate speech detection" foot_0 focuses on identifying character offsets of hate speech while handling codemixed tamil-english comments @xcite . hate speech can be extracted using a variety of methods. in this work, we approach token labeling from the perspective of hate speech detection. to detect hate speech, we assessed knn, the decision tree algorithm, and the naïve bayes-based token labeling system. this is how the remainder of the paper is structured. first, the literature on studies relevant to caste hate speech identification is briefly discussed in section 2. after a thorough description of our system in section 3, the tests and findings are reported in section 4. we wrap off by discussing potential implications for further research. ## literature review hate speech has grown to be a serious issue in today's world, with the ability to hurt both individuals and communities @xcite . using machine learning techniques to automatically identify and flag hate speech in text-based data is one possible answer to this issue @xcite . in order to prevent hate speech and objectionable content from proliferating on social media platforms, it is important to monitor the speech and information that users are disseminating. to remove these harmful elements, a strong automated filter system is needed @xcite . a wide range of topics, including politics, religion, gender, caste, race, and color, are covered by hate speech and offensive content, which has the ability to polarize society @xcite . ## problem and system description figure 1 illustrates a hate speech identification example. the aim is to determine the hate speech content given the input sentence. thuluvavellalar and agamudayar are the names of two different castes in the example above. thuluvavellalar caste members propagate hate speech directed at the agamudayar caste. once the hate speech in that specific comment has been identified, it is labeled as 1 (contains hate speech) or 0 otherwise. this dataset's description is provided in section 3.1. ## experiments and results we have presented the most effective iteration of our model after doing a number of experiments to examine the model's effectiveness. the outcomes are displayed in ## conclusion due to social media's accessibility and anonymity, as well as the shifting political situation in many regions of the world, hate speech has become more prevalent in recent years. the comment is identified as hate speech if the detection reveals that two or more of the individual outputs are positive for hatred; otherwise, it is identified as non-hate speech. through text analysis, hate and offensive content were found in this study. the technique we employed in this work was designed to predict hate speech from code-mixed tamil comments. model macro f1-score k-nearest neighbour 0.0772 decision tree 0.5862 naive bayes 0.5905