Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detection, and these solutions have yielded significant performance. However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages. Developing a classifier that captures hate speech and nuances in a low-resource language with limited data is extremely challenging. To fill the research gap, we proposeHateMAML, a model-agnostic meta-learning (MAML)-based framework that effectively performs hate speech detection in low-resource languages.HateMAMLutilizes a self-supervision strategy to overcome the limitation of data scarcity and produces better LM initialization for fast adaptation to an unseen target language (i.e., cross-lingual transfer) or other hate speech datasets (i.e., domain generalization). Extensive experiments are conducted on five datasets across eight different low-resource languages. The results show thatHateMAMLoutperforms the state-of-the-art baselines by more than 3% in the cross-domain multilingual transfer setting. We also conduct ablation studies to analyze the characteristics ofHateMAML.

Recent research has focused on using large language models (LLMs) to generate explanations for hate speech through fine-tuning or prompting. Despite the growing interest in this area, these generated explanations' effectiveness and potential limitations remain poorly understood. A key concern is that these explanations, generated by LLMs, may lead to erroneous judgments about the nature of flagged content by both users and content moderators. For instance, an LLM-generated explanation might inaccurately convince a content moderator that a benign piece of content is hateful. In light of this, we propose an analytical framework for examining hate speech explanations and conducted an extensive survey on evaluating such explanations. Specifically, we prompted GPT-3 to generate explanations for both hateful and non-hateful content, and a survey was conducted with 2,400 unique respondents to evaluate the generated explanations. Our findings reveal that (1) human evaluators rated the GPT-generated explanations as high quality in terms of linguistic fluency, informativeness, persuasiveness, and logical soundness, (2) the persuasive nature of these explanations, however, varied depending on the prompting strategy employed, and (3) this persuasiveness may result in incorrect judgments about the hatefulness of the content. Our study underscores the need for caution in applying LLM-generated explanations for content moderation. Code and results are available at https://github.com/Social-AI-Studio/GPT3-HateEval.

The rapid spread of rumors on social media and their potential impact has motivated the development of automatic rumor detection solutions. However, the existing solutions are mostly limited to detecting rumors in English which neglects the bulk of social media content in other low-resource languages. This paper aims to address the research gaps by proposing Multilingual Source Co-Attention Transformer (MUSCAT), which builds on a multilingual pre-trained language model to perform multilingual rumor detection. Specifically, MUSCAT pivots the source claims in multilingual conversation threads with co-attention transformers to improve detection performance in multilingual settings. We additionally construct multilingual rumor datasets to support our experimental evaluations. Our experimental results show that MUSCAT outperforms state-of-the-art methods in monolingual, cross-lingual, and multilingual rumor detection settings. We have also conducted empirical analysis and outlined the challenges of performing rumor detection in multilingual and cross-lingual settings.

Architecture of interconnection network plays a significant role in the performance and energy consumption of Network-on-Chip (NoC) systems. In this paper we propose NoC implementation of Midi mew-connected Mesh Network (MMN). MMN is a Minimal Distance Mesh with Wrap-around (Midi mew) links network of multiple basic modules, in which the basic modules are 2D-mesh networks that are hierarchically interconnected for higher-level networks. For implementing all the links of level-3 MMN, minimum 4 layers are needed which is feasible with current and future VLSI technologies. With innovative combination of diagonal and hierarchical structure, MMN possesses several attractive features including constant node degree, small diameter, low cost, small average distance, and moderate bisection width than that of other conventional and hierarchical interconnection networks.