S hared C on: Implicit Hate Speech Detection using Shared Semantics (2024)

Abstract: The ever-growing presence of hate speech on social network services and other online platforms not only fuels online harassment but also presents a growing challenge for hate speech detection. As this task is akin to binary classification, one of the promising approaches for hate speech detection is the utilization of contrastive learning. Recent studies suggest that classifying hateful posts in just a binary manner may not adequately address the nuanced task of detecting implicit hate speech. This challenge is largely due to the subtle nature and context dependency of such pejorative remarks. Previous studies proposed a modified contrastive learning approach equipped with additional aids such as human-written implications or machine-generated augmented data for better implicit hate speech detection. While this approach can potentially enhance the overall performance by its additional data in general, it runs the risk of overfitting as well as heightened cost and time to obtain. These drawbacks serve as motivation for us to design a methodology that is not dependent on human-written or machine-generated augmented data for training. We propose a straightforward, yet effective, clustering-based contrastive learning approach that leverages the shared semantics among the data.

## introduction warning: this paper contains content that may be offensive or upsetting. detecting hate speech is more challenging compared to other tasks due to its varying perception and definition among individuals, which is reflected in the inconsistencies between datasets @xcite . implicit hate speech, in particular, often lacks explicit cues such as swear words or insults, figure 1 : an example of shared semantics among posts in the ihc dataset. we often find a representative post among posts sharing similar semantics, which is more accurate than human-written implications. the highlighted portion represents the common meaning shared among these posts. in the realm of implicit hate speech detection, the sota performance has been achieved by contrastive learning (cl) with humman-written implications or machine-generated augmented texts. however, there are some limitations to these approaches: (1) it requires experts to manually generate implications, which is expensive. for example, for an implicit hate speech dataset like ihc @xcite , the total annotation cost was $15k and it took a considerable amount of time to complete the dataset construction due to the complexity and scale of the work involved in developing a resource for understanding implicit hate speech. this limitation significantly restricts the scalability of this approach when applied to a wide range of datasets or different domains @xcite . furthermore, (2) it is hard to say that da approach uses the correct positive pairs. the da strategy merely employs post-augmentation as a positive pair, which lacks semantic consistency across posts. figure 2 shows the comparison between the existing cl approaches and our methodology. in order to address implication costs and enhance the existing approaches, we propose sharedcon, a simple yet effective technique-leveraging shared semantics from the training data itself without additional data construction steps or extra modules. we extract shared semantics of training datasets by grouping posts and consider the centroid post of each group as the representative semantics shared by the posts. sharedcon is evaluated on three hate speech benchmark datasets in both in-dataset and cross-dataset settings. experimental results demonstrate that sharedcon outperforms our baselines with a 0.43%p average improvement in the indataset setting and 1.43%p average improvement in the cross-dataset setting. furthermore, we compare the performance of sharedcon with current sota approach (impcon). the results show that sharedcon can deliver comparative (even outperform for some cases) performance, against the current sota approach that requires a human-laborintensive step. our empirical analysis displays that utilizing shared semantics in the quality-assured sentence embedding space can work as effective as manually produced implications. while the representation of shared semantics works similarly to the implication in @xcite , our process provides the flexibility to train the model on various datasets, even those without human-annotated implications. the approach is simple yet effective without extra modules; we cluster the training data and select one post as a core sample of shared semantics, then the selected post plays the role of the anchor in the cl step. ## contrastive learning with shared semantics while the idea of utilizing shared semantics in the fine-tuning process is promising for implicit hate speech detection, one major drawback is that it requires a dataset with implications, which are often written by humans manually. therefore, we study an effective way of finding shared semantics without human involvement and improving the cross-dataset performance. ## analysis our discussion focuses on validating the hypothesis that shared semantics should be identified from the centroid of each cluster, based on a twofold additional scenarios. 1) in section 4.1, we randomly select a post and allocate it as an anchor; and 2) in section 4.2, we choose one sentence among the top three or five sentences that are most proximal (in terms of the euclidean distance) to the centroid of the cluster as an anchor. ## related work hate speech detection there are several datasets on explicit hate speech with different purposes @xcite @xcite . @xcite addressed distinguishing hate speech from offensive language by using a crowd-sourced hate speech lexicon. implicit hate speech, on the other hand, characterized by its subtle linguistic expressions, presents a more complex obstacle for detection compared to its explicit hate speech @xcite . recently, researchers have started considering implicit hatred or abusiveness. @xcite introduced a novel generic framework, macas, for online abusive language detection across different domains. @xcite addressed the challenges in detecting implicitly abusive language, highlighting the limitations of existing datasets and 10451 label: 1 ## conclusions we have enhanced the existing implicit hate speech detection approach, which previously relied on human-generated implications. this improvement involves clustering sentence embeddings and utilizing the centroid sentence of each cluster as positive examples during the contrastive learning process. the experimental results have demonstrated that posts in the dataset have shared semantics and our approach of using them as positive samples for contrastive learning works well. particularly, our methodology has shown competitive or superior performance improvement of fine-tuned models in cross-dataset experiments. to sum up, our approach has enhanced model generalization without human-written implications, providing a better solution for implicit hate speech detection in terms of cost.