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We introduce **RoadSocial, a large-scale, diverse VideoQA dataset tailored for generic road event understanding from social media narratives**. Unlike existing datasets limited by regional bias, viewpoint bias and expert-driven annotations, RoadSocial captures the global complexity of road events with varied geographies, camera viewpoints (CCTV, handheld, drones) and rich social discourse. Our scalable semi-automatic annotation framework leverages Text LLMs and Video LLMs to generate comprehensive question-answer pairs across 12 challenging QA tasks, pushing the boundaries of road event understanding. RoadSocial is derived from social media videos spanning **14M frames** and **414K social comments**, resulting in **a dataset with 13.2K videos, 674 tags and 260K high-quality QA pairs**. We **evaluate 18 Video LLMs (open-source and proprietary, driving-specific and general-purpose)** on our road event understanding benchmark. We also demonstrate RoadSocial's utility in improving road event understanding capabilities of general-purpose Video LLMs.