Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing |
| Editors | Houda Bouamor, Juan Pino, Kalika Bali |
| Publisher | ACL Anthology |
| Pages | 15986-16008 |
| Number of pages | 23 |
| ISBN (Electronic) | 9798891760608 |
| ISBN (Print) | 9798891760608 |
| DOIs | |
| Publication status | Published - 2023 |
Funding
This research was funded by the Netherlands Organisation for Scientific Research (NWO) through the Hybrid Intelligence Centre via the Zwaartekracht grant (024.004.022). We would like to thank the ARR reviewers for their feedback.
| Funders | Funder number |
|---|---|
| Nederlandse Organisatie voor Wetenschappelijk Onderzoek | |
| Hybrid Intelligence Centre | 024.004.022 |
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