Baseline predictors of response to a group-based speech and communication intervention in Parkinson's disease: A secondary analysis of a randomized controlled trial.

Nowadays, internet users can anonymously post content, including targeted offensive remarks, with their identities often unknown to victims or readers. Large language models (LLMs) can also generate offensive content. A recent incident involving the Grok AI chatbot exemplifies this issue. Such content may negatively affect those who encounter it, especially targeted users. Offensive language, including hate speech, is widely recognized as a significant source of harm. It can result in human rights violations, personal harm, and, in some cases, criminal behavior. It may also pose serious risks, such as suicide attempts. According to Ref, offensive language generally refers to any expression that is hurtful, insulting, morally repugnant, or degrading. Hate speech, which is a subset of offensive language, targets people based on characteristics such as sexual orientation, gender, or race. In this study, following, we define offensive language as any comment containing a threat, insult, harmful or discriminatory action, swear words, profane language, or improper speech that violates accepted standards of normal discourse, whether targeted or not. Numerous studies, including, confirm that hate speech significantly contributes to online abuse and harassment. In contrast, notes that some countries, including Canada and France, have implemented laws prohibiting hate speech. A range of resources and datasets has been developed to support research in this field, along with proposed models to address this issue. While some approaches rely on Transformer-based architectures, others investigate LLMs’ ability for hate speech detection. However, many existing datasets do not provide rationales, and most models lack interpretability. Moreover, most existing models fail to offer transparent and trustworthy justifications for their classifications. This limitation is significant because platform moderators require explainable tools to understand why specific content violates policy guidelines. A new study on an understudied languageargues that a large disparity exists between low-resource and high-resource languages from a performance perspective. On the other hand, states that a significant research gap exists in detecting hate speech in low-resource languages, mainly due to linguistic diversity and the limited availability of annotated datasets. Somali, a low-resourced language, lacks many natural language processing (NLP) resources. Consequently, a language rich in literature and poetry remains underrepresented in modern computational linguistics research. To address this gap and contribute to the creation of offensive-free social media and LLMs, we introduce XOLDS, a novel dataset with human-annotated rationales for offensive language detection in Somali. This dataset is not only the largest to date but also the first in the Somali language to include human-annotated rationales. Our dataset comprises 10,175 samples manually collected from TikTok and YouTube, with additional samples gathered from X. Three annotators labeled each sample as offensive or not offensive. Additionally, two of the three annotators selected relevant text segments and provided rationales for their annotations. Using these annotations, we then developed SomOffXplain (Benchmarking Explainable Offensive Language Detection in Somali with Human-Annotated Rationales), an interpretable framework for offensive language detection in Somali. To improve interpretability of SomOffXplain, we adopted class-conditioned contrastive learning and benchmarked the framework’s performance against five fine-tuned models using Local Interpretable Model-Agnostic Explanations (LIME), evaluating both explainability and classification accuracy. SomOffXplain implements explainability via extractive span-level rationales, where the model identifies the word or phrase in an input text that is most predictive of the offensive language class. Specifically, it performs span-level rationale extraction and highlights the exact text segments that support its predictions. We further studied the rationale generation capability of four LLMs using zero-shot and few-shot (2-shot) settings. We evaluated the effectiveness of LLMs in explainability at both the word and phrase levels for rationale generation. Finally, this work proposes the first rationale-based benchmark dataset for the Somali language. We develop a computationally less expensive explainable framework for Somali offensive language detection. By comprehensively evaluating various methods, we create a new benchmark for explainable offensive language detection in Somali that can also be extended to other low-resource languages. Thus, we believe our work makes a significant contribution to the NLP community, particularly by addressing barriers faced by low-resource languages. The main contributions of this paper are as follows: