Towards Explainable Hate Speech Detection (2025)

Abstract: Recent advancements in deep learning have significantly enhanced the efficiency and accuracy of natural language processing (NLP) tasks. However, these models often require substantial computational resources, which remains a major drawback. Reducing the complexity of deep learning architectures, and exploring simpler yet effective approaches can lead to cost-efficient NLP solutions. This is also a step towards explainable AI, i.e., uncovering how a particular task is carried out. For this analysis, we chose the task of hate speech detection. We address hate speech detection by introducing a model that employs a weighted sum of valence, arousal, and dominance (VAD) scores for classification. To determine the optimal weights and classification strategies, we analyze hate speech and non-hate speech words based on both their individual and summed VAD-values. Our experimental results demonstrate that this straightforward approach can compete with state-of-the-art neural network methods, including GPT-based models, in detecting hate speech.

## introduction natural language processing is based on concepts and studies in various fields such as computer science, linguistics, and artificial intelligence, and aims to give computer systems the ability to understand, generate, and interpret human language (chowdhary, 2020). tasks such as speech recognition, text summarization, and question answering greatly benefit from various nlp algorithms @xcite @xcite . in recent years, significant advancements have been made in natural language processing thanks to the developments in the field of deep learning @xcite . in particular, the introduction of the transformer architecture has led to improvements in text generation, machine translation, and text summarization @xcite . however, despite their accuracy, deep learning models require substantial computational resources and long training times. although transfer learning can reduce training costs, the high computational demands remain a significant drawback @xcite . we can avoid the high cost associated with expensive hardware requirements and timeconsuming training procedures if a traditional algorithm can achieve similar or even better results while solving an nlp task. motivated by this idea, and recognizing the value of exploring simple yet effective approaches alongside proposing new methods to enhance existing techniques, we examine whether an existing concept can be modified to outperform or compete with neural networks-based strategies. for the analysis, we select the text classification task of hate speech detection since numerous complex learning-based methods are present in this field and it holds many challenges reflecting problems encountered in other nlp tasks as well @xcite . one of the great challenges of hate speech detection is the usage of informal language @xcite . informal language is more figurative which might be the reason of preferring neural network models over traditional algorithms since neural networks-based models achieve high performance in understanding the meaning of a word within its context. another challenge is the fact that perceiving content as harmful is a relatively subjective task. exactly defining hate speech is troublesome @xcite . several studies classify a text as hate speech if it negatively targets individuals or groups, and hurts or degrades them because of the characteristics of a group they belong to such as sexuality, religion, or nationality, and thus spreads or shows hate towards a certain group @xcite @xcite . based on the idea that hate speech demonstrates negative sentiments, we present a simple learning-free algorithm that uses a sum of valence, arousal, and dominance values @xcite to classify a given text as hate speech or non-hate speech. according to our experiments, our simple yet effective method can compete with neural networks-based methods, including recent large language models. the remainder of the paper is organized as follows. in sec. 2, we give a brief summary of methods utilized in our experiments. in sec. 3, we present the vad algorithm, and in sec. 4, we demonstrate our experimental results. we conclude with a summary and discussion of future directions in sec. 5 and address the limitations of our study in sec. 6. ## related work before neural networks became widely used in the field of hate speech detection, mostly traditional machine learning methods such as naive bayes were used @xcite @xcite . as described by @xcite , these early approaches typically rely on feature extraction methods such as bag-of-words @xcite . for instance, the model introduced by @xcite applies rule-based sentiment analysis techniques to detect hate speech. in the remainder of this section, we briefly review only the models, and lexicons and datasets which we utilize in our experiments. for more detailed information about hate speech detection, we kindly ask the reader to refer to comprehensive surveys @xcite @xcite . ## proposed approach our approach is based on the vad model which is also widely used in psychology to measure emotions @xcite . the vad model represents emotions in a three-dimensional space. the first dimension, valence, quantifies the positivity or negativity of an emotion. the second dimension, arousal, measures the activeness or passiveness of the emotion. the third dimension, dominance, quantifies the degree of control one feels over a situation. for instance, calmness can be characterized by high valence, low arousal, and high dominance, whereas anger is typically associated with low valence, high arousal, and high dominance. in the remainder of this section, we first analyze the vad values of commonly used words in hate speech, and then we introduce our method, called vad-baseline. ## experiments in this section, we first present our experimental setup. then, we provide our experimental results and discuss the results. lastly, we detail our parameter selection procedure. ## conclusion natural language processing has greatly benefited from learning-based approaches, particularly transformer models, which have enabled highly accurate performance in tasks such as question answering and text summarization. however, these come with high computational costs, mainly due to their training processes, which often require expensive hardware and a significant amount of time. on the other hand, traditional algorithms do not require any training phase, making them generally more computationally efficient than neural networks-based models. motivated by this fact and taking into account that it is important to explore explainable simple yet effective approaches to improve existing techniques alongside developing new methods, in this paper, we investigate whether low-cost traditional algorithms can compete or even outperform neural networks-based models in the field of natural language processing. we chose hate speech detection for our investigation because it reflects challenges common to many nlp tasks, including informal language use and subjectivity, and has been the focus of numerous complex learningbased models. since hate speech is characterized by negative sentiments, we propose a learning-free method that leverages valence, arousal, and dominance values to classify texts as hate speech or not. in the experiments, our vad-baseline algorithm demonstrates that simple yet effective methods can compete with recent neural networks-based models. hence, we argue that further analysis of low-cost explainable methods can help us to obtain robust algorithms while avoiding the computational burden of extensive training. furthermore, neural networks-based models can benefit from the observations we obtain from improving classical methods. as future work, we plan to refine the vad-baseline algorithm by making the selection of weights and classification strategies adaptive, as well as enhancing it to address implicit hate speech. also, we will investigate different natural language processing tasks and existing traditional methods to analyze whether we can obtain accurate results without high computational costs.