Integrated Gradients as Proxy of Disagreement in Hateful Content
Authors: Alessandro Astorino, Giulia Rizzi, Elisabetta Fersini (Università di Milano Bicocca, Universitat Politècnica de València, 2023)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Online platforms have increasingly become hotspots to spread not only opinions but also hate speech, posing substantial obstacles to developing constructive and inclusive online communities. In this paper, we propose a novel approach that leverages the integrated gradients of pre-trained language models to automatically predict both hate speech and the potential disagreement that can arise from readers. The integrated gradient attributions are used to shed light on the model’s decision-making process, attributing importance scores to individual tokens and enabling the identification of crucial factors contributing to disagreement and hate speech classifications. The integrated gradients’ straightforwardness allows for the recognition of fundamental causes of disagreements and hate speech content. By adopting an interpretable approach, we bridge the gap between model predictions and human comprehension. Our experimental results highlight the effectiveness of our approach, outperforming traditional BERT models and state-of-the-art methods in both prediction tasks.

Introduction
In the modern era, human beings are constantly absorbing content of various kinds generated and shared on the web. To ensure the sustainability of this continuous flow of information and promote individual and societal well-being in the online context, it is important to recognize where hate content can harm on a personal level. Individuals, based on their cultural beliefs and backgrounds, may have varying susceptibility to potentially offensive content. Thus, it is necessary to safeguard different perspectives by developing Natural Language Processing (NLP) models capable of capturing and modeling diverse perceptions. Addressing disagreement, particularly in relation to hate speech detection, has garnered increasing interest in recent years [1, 2, 3, 4]. Although numerous approaches have been developed to handle disagreement in hate speech detection [5, 6, 7, 8], few have truly focused on modeling perspectivism. Identifying potential disagreements within hateful content, especially in recognizing controversial elements, is crucial for several reasons. When the possibility of disagreement arises in hateful texts shared on social media platforms like Twitter, it becomes essential to have a service that can recognize if the content might cause disagreement and act as a filter based on personal perspectives. Moreover, a detoxification strategy could be implemented to notify authors of user-generated content, cautioning them about the potential perception of their text as hateful by certain readers and suggesting revisions. Recognizing disagreements within hateful sentences and determining the associated disagreement-related elements can significantly contribute to the creation of reliable benchmarks. For content prone to disagreements, specific annotation policies can be implemented, such as involving more annotators or excluding such samples from datasets. Annotators could also be guided with cues to focus on particular elements that may be perceived differently by readers, like words, hashtags, or emojis linked to disagreement-related elements. In this paper, we aim to connect hate speech and disagreement by identifying which hateful elements contribute most to predicting disagreement. We combine pre-trained language models and integrated gradients, providing key contributions: a filtering strategy for textual constituents that helps explain hateful messages and a unified model that, by predicting hateful content and explaining it, forecasts if disagreement may arise from the content. The rest of the paper is organized as follows: Section 2 offers an overview of the state of the art, Section 3 describes the datasets, Section 4 details the proposed approach, Section 5 presents the results, and Section 6 concludes with future research directions.

Conclusion
The proposed paper introduces a novel approach for detecting disagreement within hateful content, leveraging integrated gradients from pre-trained language models to predict both hate speech and potential disagreement among diverse readers. The approach is evaluated on four benchmark datasets related to Learning With Disagreements, and results show that it outperforms the baseline m-BERT model in predicting disagreement. Notably, one of the proposed strategies, NN + Dist, performs exceptionally well, achieving statistically significant improvements over the m-BERT-based baseline. Overall, this approach demonstrates promising potential for predicting disagreement within hateful content compared to traditional BERT models. Future work could explore the applicability of this approach across other languages and expand its scope to include multimodal data analysis, given the growing prevalence of images and videos in online communication.