  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.
