We present Reddit-V, a new dataset designed to advance research on social media virality prediction in natural language processing. The dataset consists of over 27,000 Reddit posts, each enriched with images, textual content, and pre-engagement metadata such as post titles, categories, sentiment scores, and posting times. As an initial benchmark, we evaluate several instruction-tuned large language models (LLMs) in a zero-shot setting, prompting them with post titles and metadata to predict post virality. We then fine-tune two multimodal models, CLIP and IDEFICS, to assess whether incorporating visual context enhances predictive performance. Our results show that zero-shot LLMs perform poorly, whereas the fine-tuned multimodal models achieve better performance. Specifically, CLIP outperforms the best-performing zero-shot LLM (CodeLLaMA) by 3%, while IDEFICS achieves an 7% improvement over the same baseline, highlighting the importance of visual features in virality prediction. We release the Reddit-V dataset and our evaluation results to facilitate further research on multimodal and text-based virality prediction. Our dataset and code will be made publicly available on Github

Recent efforts in natural language processing (NLP) commonsense reasoning research have led to the development of numerous new datasets and benchmarks. However, these resources have predominantly been limited to English, leaving a gap in evaluating commonsense reasoning in other languages. In this paper, we introduce the ArabicSense Benchmark, which is designed to thoroughly evaluate the world-knowledge commonsense reasoning abilities of large language models (LLMs) in Arabic. This benchmark includes three main tasks: first, it tests whether a system can distinguish between natural language statements that make sense and those that do not; second, it requires a system to identify the most crucial reason why a nonsensical statement fails to make sense; and third, it involves generating explanations for why statements do not make sense. We evaluate several Arabic BERT-based models and causal LLMs on these tasks. Experimental results demonstrate improvements after fine-tuning on our dataset. For instance, AraBERT v2 achieved an 87% F1 score on the second task, while Gemma and Mistral-7b achieved F1 scores of 95.5% and 94.8%, respectively. For the generation task, LLaMA-3 achieved the best performance with a BERTScore F1 of 77.3%, closely followed by Mistral-7b at 77.1%. All codes and the benchmark will be made publicly available at https://github.com/.