Supplementary Material.xlsx

As social platforms see a surge in diverse user-generated content (UGC), determining high-quality contributions becomes crucial, particularly for educational purposes. This paper explores how the quality of educational UGC shapes user experiences, engagement, and credibility, proposing a computational framework called ELECTRE-SORT. Using a dataset of 16 educational UGCs from various platforms, we categorize content based on quality criteria and find that the majority fall into the medium quality category. This underscores the importance of prioritizing quality over quantity for platform success. Finally, we provide a sensitivity analysis and suggest future research directions.

随着社交平台上多样化的用户生成内容（User-Generated Content, UGC）激增，甄别高质量贡献内容变得至关重要，在教育场景中尤为如此。本研究探讨了教育场景下的UGC质量如何影响用户体验、参与度与可信度，并提出了一款名为ELECTRE-SORT的计算框架。本研究使用来自多个平台的16条教育UGC数据集，依据质量标准对内容进行分类，结果显示大部分内容归为中等质量层级。这一结果凸显了对于平台发展而言，相较于内容数量，优先保障内容质量的重要性。最后，本研究开展了敏感性分析，并提出了未来的研究方向。