Social media virality significantly shapes public discourse during geopolitical conflicts, where emotionally charged and multimodal content can rapidly gain widespread attention. However, most prior approaches rely on retrospective engagement signals, limiting their usefulness for early prediction. Multimodal virality modeling in high-stakes Arabic discourse remains largely unexplored. We introduce NakbaVirality, a shared task on multimodal virality classification in conflict-related social media posts, organized as part of the Nakba-NLP workshop at LREC 2026. The dataset consists of 2,600 anonymized posts from X and Reddit collected after October 7, 2023, each including text, an associated image, and normalized engagement labels. Participants must classify posts into low, medium, or high virality categories using only textual and visual inputs. The task provides standardized splits, baseline systems, and evaluation using macro-F1 and accuracy. NakbaVirality establishes the first benchmark for multimodal virality prediction in Arabic high-stakes discourse and promotes research on contextual and multimodal modeling for early impact prediction. The shared task attracted 18 participants, who contributed a total of 4 official test phase submissions.

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 is publicly available.

The generation of highly fluent text by Large Language Models (LLMs) poses a significant challenge to information integrity and academic research. In this paper, we introduce the Multi-Domain Detection of AI-Generated Text (M-DAIGT) shared task, which focuses on detecting AI-generated text across multiple domains, particularly in news articles and academic writing. M-DAIGT comprises two binary classification subtasks: News Article Detection (NAD) (Subtask 1) and Academic Writing Detection (AWD) (Subtask 2). To support this task, we developed and released a new large-scale benchmark dataset of 30,000 samples, balanced between human-written and AI-generated texts. The AI-generated content was produced using a variety of modern LLMs (e.g., GPT-4, Claude) and diverse prompting strategies. A total of 46 unique teams registered for the shared task, of which four teams submitted final results. All four teams participated in both Subtask 1 and Subtask 2. We describe the methods employed by these participating teams and briefly discuss future directions for M-DAIGT.

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