Hate speech poses significant challenges to maintaining healthy online conversations, and automated systems are crucial for its accurate detection and mitigation. In this paper, we (CNLP-NITS-PP) introduce ATLANTIS (Attentive Transformer-LSTM for Named Entity and Token Identification System), a robust model designed to address the pervasive issue of hate speech in online social media platforms. ATLANTIS focuses on hate span identification within sentences labeled as hate speech, framed as a sequence labeling task using BIO notation. Leveraging a Hate dataset enriched with Named Entity Recognition (NER) tags, ATLANTIS efectively identifies hate speech spans within the text by combining contextualized representations and sequential modeling. The empirical results showcase ATLANTIS's efectiveness in isolating explicit signs of hate from a contextual backdrop, ofering a promising solution for creating safer online environments. We achieve a macro F1 score of 0.488 on the public test set and 0.508 on the private test set. This work not only lays the foundation for future advancements in hate-span detection but also emphasizes the importance of model eficiency, interpretability, and expanded training data that encompass diverse linguistic nuances and evolving hate speech trends. Code is available at https://github.com/niyarrbarman/hasoc23 htp:/ceur-ws.org CEUR Workshop Proceedings (CEUR-WS.org) ISN1613-073
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Social media platforms like Twitter and Facebook have become commonplace in modern life,
giving people worldwide easy access to voice their thoughts and connect. However, the open
nature of these platforms also allows harmful content like hate speech, harassment, and threats
aimed at vulnerable groups to spread [
A significant hurdle is that ofensive content can take many linguistic forms, necessitating
context-aware models to pinpoint the specific snippets of text that render a post hateful or
abusive [
This paper tackles the problem of hate span identification within sentences labelled as hate
speech in the HASOC 2023 [
The HASOC dataset provides ground truth BIO tag sequences for abusive sentences from
public hate speech sources [
We present our proposed model design and tactic for the hate span identification task,
harnessing contextualised representations and sequential modelling [12]. Results showcase
our techniques’ eficacy in isolating explicit signs of hate from a contextual backdrop. By
classifying specific linguistic cues and semantic relationships that encode hate, our method
provides insights into the underlying fabric of abusive language [
The research presented in this paper holds significant promise in tackling the pervasive problem of hate speech on online social media platforms. ATLANTIS, the hate span detection system that has been developed, carries practical implications for content moderation, user safety, and the improvement of online discussions. By precisely identifying and extracting hate spans from hateful sentences, ATLANTIS equips social media platforms to more eficiently filter and eliminate hateful content, thereby promoting a safer and more inclusive online environment. Furthermore, this technology can serve as a valuable tool for gaining insights into the prevalence and dynamics of hate speech, assisting researchers and policymakers in formulating evidencebased strategies to combat online hatred.
This research paper is intended for a diverse audience encompassing various stakeholders concerned with the detection and mitigation of hate speech. Content moderators and social media platform administrators will find valuable insights and methodologies within as they work towards maintaining respectful and secure online communities. Researchers in the ifelds of natural language processing (NLP) and machine learning will appreciate the detailed methodology and architecture of the ATLANTIS model, which represents an advancement in state-of-the-art hate span detection. Policymakers and organizations focused on addressing online hate speech will also gain valuable insights into the potential of machine learning-based solutions for addressing this pressing issue. Furthermore, educators and students studying NLP, machine learning, and technology ethics can utilize this paper as a resource for understanding the development and application of advanced models for hate speech detection. Ultimately, this research paper aims to engage a broad and diverse audience, fostering collaboration and innovation in the ongoing efort to create safer online spaces.
The primary objective of this research is to create a hate span detection system capable of pinpointing and extracting uninterrupted sequences of tokens found within hateful sentences, which we refer to as “hate spans”. These hate spans are characterized as consecutive sets of tokens within a sentence that collectively expresses explicit hatefulness. The aim of this shared task is to automatically identify and extract all such hateful spans from preprocessed sentences. The hate span detection task is approached as a sequence labeling problem, wherein each token in a sentence is labeled with a specific tag to indicate its association with a hateful span. The labeling follows the BIO notation, with ‘B’ signifying the beginning of a hate span, ‘I’ denoting the continuation of a hate span, and ‘O’ indicating all other tokens that are not part of any hate span within the sentence.
The goal is to develop a machine-learning model to accurately predict the correct sequence of BIO tags for each token in a given sentence, efectively detecting and delineating hate spans within the text.
The methodology employed to address the issue of hate speech at scale through the ATLANTIS model comprises a systematic approach encompassing data preprocessing, tokenization, model architecture, and the classification process. Leveraging the HateNorm23 dataset, which features text samples paired with Named Entity Recognition (NER) tags categorizing each word as ‘B’ (signifying the start of a hate span), ‘I’ (indicating inclusion within a hate span), or ‘O’ (denoting other), we conduct word-level tokenization to segment the text into meaningful units. A custom tokenizer is then fine-tuned on the dataset to tailor tokenization for hate span detection. The ATLANTIS model adopts a multi-stage architecture, initially processing tokenized text through a custom transformer section followed by a bidirectional long short-term memory (Bi-LSTM) [13] section. The transformer captures contextual information and relationships, while the Bi-LSTM captures sequential dependencies. Subsequently, fused representations from these sections traverse fully connected layers for the conclusive classification task. Detailed insights into the architecture, hyperparameters, and experimental findings will be presented to substantiate ATLANTIS’s eficacy in mitigating hate speech at scale.
ATLANTIS consists of three primary components: Transformer Encoder Block: The Transformer [14] block is a foundational component for capturing contextual relationships within sequences. Its self-attention mechanism enables the model to weigh the significance of each word in relation to others, allowing it to understand complex dependencies and semantic connections. This block excels at learning hierarchical features from the input data, providing a solid basis for understanding the underlying patterns in the sequential data, which is particularly crucial in NLP tasks.
BiLSTM Layer: The BiLSTM layer complements the Transformer’s strengths by efectively capturing sequential dependencies in the data. By incorporating a BiLSTM layer, the model can capture fine-grained temporal relationships and contextual nuances that might be missed by the Transformer alone. This is especially valuable for NER, where identifying entities often relies on sequential patterns.
Sequential Block with FC Layers: The Sequential Block, containing Fully Connected layers, serves as a vital element for transforming the enriched features from the preceding blocks into a suitable format for making predictions. These FC layers allow for nonlinear transformations and higher-level abstractions, enabling the model to learn complex mappings from the learned representations to the target NER labels.
Engineering Decisions: We aimed to identify a solution that excels in performance and eficiency. Our approach led us to employ a sequence of six transformer blocks. Upon extending the number of blocks, we observed a period during which the F1 score plateaued, roughly around 9 to 10 blocks. Subsequently, the score rapidly declined, indicative of overfitting taking hold.
Regarding the BiLSTM layers, we integrated a single BiLSTM layer for the ultimate modeling phase. Elevating the count of BiLSTM layers increased the model’s complexity, rendering it more challenging to train and subsequently slowing down inference processes.
We settled on a configuration of num_heads = 4 for the transformer block. Introducing additional num_heads led to a stage of diminishing returns. Given the limited size of our dataset, the model tended to memorize the training data rather than exhibiting the capacity to generalize to novel data. This phenomenon, in turn, resulted in overfitting or diminished performance.
Adam was used as the optimizer with learning_rate = 1e-3
The dataset [15] comprises a total of 2421 data points. We partitioned this dataset into an 80:10:10 ratio, allocating segments for training, validation, and testing purposes. Within the dataset, a sum of 8165 distinct words can be found. The visualization of the dataset is presented in Figure 2. Notably, hate speech constitutes 17.422% of the entire dataset.
In this section, we present the results of our experiments, organized into three subsections: Baseline Methods, Intrinsic Results, and Extrinsic Results. We discuss the models we used in the Baseline Methods section and provide details on the intrinsic and extrinsic performance of our approach.
To establish a benchmark for our experiments and assess the efectiveness of our proposed method, we employed the following baseline models: Pretrained BERT: BERT [12] has shown remarkable success in various natural language processing tasks, and we included it as a reference to evaluate the performance of our approach against a state-of-the-art model.
Transformer Encoder: The incorporation of the Transformer [14] Encoder, in our study serves a dual purpose. Firstly, it provides a reference point for evaluating the performance of our approach. Secondly, it underscores the efectiveness of the encoder layers, equipped with self-attention mechanisms, which play a key role in the remarkable success of BERT and similar models across various natural language processing tasks.
BiLSTM: BiLSTM [13] networks have been widely used for sequence labeling tasks, and we included this baseline to evaluate our approach against a more traditional sequence labeling model.
In this subsection, we present the intrinsic results of our approach to the validation set. We discuss the performance of our model and provide a detailed analysis of the results.
Our model’s performance on the validation set was evaluated using various metrics, including precision, recall and F1-score. They have been presented in Table 2.
In this subsection, we present the extrinsic results of our approach to the competition test set. We report public and private test scores, commonly used in Kaggle competitions to evaluate model performance on unseen data. Table 3 summarizes our model’s public and private test scores and compares them with the baseline models.
In this section, we review several relevant studies that contribute to the understanding and development of hate speech detection, ofensive language detection, and related natural language processing tasks. These works collectively provide insights into various approaches and techniques employed in this field.
In Qian et al.’s (2019)[16] study [14], a new challenge called generative hate speech intervention was introduced. The authors augmented their research with two comprehensive datasets obtained from Reddit and Gab, which contained intervention responses collected from crowdsourcing. The assessment of three generative models, specifically Seq2Seq, VAE, and RL, revealed areas where hate speech intervention methods could be enhanced.
In the work conducted by Alshalan et al. [17], they tackled the problem of hate speech in the Arabic Twittersphere. They introduced a dataset consisting of 9316 tweets categorized into hate speech, abuse, and normalcy. Their assessment encompassed various models, including CNN, GRU, CNN + GRU, and BERT. Among these models, CNN emerged as the most efective, achieving superior performance with an F1-score of 0.79 and an AUROC of 0.89.
In the research conducted by Elalami et al. [18], they introduced a transfer learning strategy for detecting ofensive language in multiple languages. This approach leveraged several BERT models, such as BERT, mBERT, and AraBERT. Their results were outstanding, surpassing the performance of current leading methods that employ joint-multilingual and translationbased approaches. This study underscored the robustness of BERT models in the context of Multilingual Ofensive Language Detection.
Ozler et al. [19] explored the application of BERT for multi-label and multi-domain incivility detection tasks. They successfully established a new state-of-the-art performance across various datasets. The study suggested that direct data combination from multiple domains yielded superior results compared to more intricate training methods.
The study by Hoang et al. [20] introduced ViHOS, a novel Vietnamese dataset for hate and ofensive span detection, containing 26,467 annotated spans in 11,056 comments. Baseline models, including XLM-RBase, XLM-RLarge, PhoBERTBase, and PhoBERTLarge, were evaluated, with the XLM-RLarge model leading with an F1-score of 0.7770. The study found that detecting multiple spans outperformed single-span detection in Vietnamese hate speech.
Lample et al. [
Ma (2016) [21] presented a neural network architecture for sequence labeling, representing an end-to-end model without needing task-specific resources, feature engineering, or data preprocessing. The study attained state-of-the-art performance on two linguistic sequence labeling tasks, outperforming prior state-of-the-art systems.
Peters et al. (2017) [22] proposed a simple semi-supervised approach using pre-trained neural language models to enhance token representations in sequence tagging models. Their approach consistently outperformed state-of-the-art models in NER and Chunking datasets. Notably, the study showed that including both forward and backward language models consistently improved performance.
These related works collectively contribute valuable insights and methodologies that inform the development of hate speech detection and associated natural language processing tasks, showcasing the advancements and challenges in this field.
In this research, we have presented ATLANTIS (Attentive Transformer-LSTM for Named Entity and Token Identification System), a robust model designed to combat hate speech at scale. Leveraging a Hate dataset with detailed Named Entity Recognition (NER) tags, ATLANTIS efectively identifies hate speech spans within textual content. Our multi-stage architecture, comprising a custom transformer and bidirectional LSTM, captures contextual information and sequential dependencies, facilitating precise hate span classification. Empirical results demonstrate ATLANTIS’s efectiveness in this critical task. As we continue to address the pressing issue of hate speech in digital spaces, ATLANTIS ofers a promising solution for safer online environments.
The work presented here lays the foundation for future advancements in hate span detection. Further improvements in model eficiency and interpretability, along with expanded training data encompassing diverse linguistic nuances and evolving hate speech trends, hold promise. Investigating the integration of real-time monitoring and incorporating user-specific context may enhance the model’s capabilities in dynamically changing online environments. Additionally, exploring multilingual and cross-platform hate speech detection is vital for broader impact. As technology evolves, ATLANTIS and its successors are poised to play a pivotal role in fostering safer, more inclusive digital spaces.
We wish to extend our appreciation to the Computer Science and Engineering Department of the National Institute of Technology Silchar for granting us the opportunity to carry out our research and experiments. We are grateful for the support, resources, and research environment ofered by the CNLP & AI Lab at NIT Silchar.