The NakbaVirality Shared Task on MultimodalVirality Prediction in High-Stakes Discourse

Abstract

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 NakbaNLP 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 5 official test phase submissions.

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