Disagreements are common in online discussions. Disagreement may foster collaboration and improve the quality of a discussion under some conditions. Although there exist methods for recognizing disagreement, a deeper understanding of factors that influence disagreement is lacking in the literature. We investigate a hypothesis that differences in personal values are indicative of disagreement in online discussions. We show how state-of-the-art models can be used for estimating values in online discussions and how the estimated values can be aggregated into value profiles. We evaluate the estimated value profiles based on human-annotated agreement labels. We find that the dissimilarity of value profiles correlates with disagreement in specific cases. We also find that including value information in agreement prediction improves performance.

翻译：分歧在在线讨论中普遍存在。在某些条件下，分歧可能促进协作并提高讨论质量。尽管已有识别分歧的方法，但文献中缺乏对影响分歧因素的深入理解。本研究探讨了一个假设：个人价值观的差异能够预示在线讨论中的分歧。我们展示了如何利用先进模型估算在线讨论中的价值观，以及如何将估算的价值观聚合为价值轮廓。基于人工标注的一致标签，我们对估算的价值轮廓进行了评估。研究发现，在特定案例中，价值轮廓的差异性与分歧存在相关性。此外，将价值观信息纳入一致预测可提升预测性能。