  (Dis)agreement detection aims to identify the authors' attitudes or positions
(\textit{{agree, disagree, neutral}}) towards a specific text. It is limited
for existing methods merely using textual information for identifying
(dis)agreements, especially for cross-domain settings. Social relation
information can play an assistant role in the (dis)agreement task besides
textual information. We propose a novel method to extract such relation
information from (dis)agreement data into an inductive social relation graph,
merely using the comment-reply pairs without any additional platform-specific
information. The inductive social relation globally considers the historical
discussion and the relation between authors. Textual information based on a
pre-trained language model and social relation information encoded by
pre-trained RGCN are jointly considered for (dis)agreement detection.
Experimental results show that our model achieves state-of-the-art performance
for both the in-domain and cross-domain tasks on the benchmark -- DEBAGREEMENT.
We find social relations can boost the performance of the (dis)agreement
detection model, especially for the long-token comment-reply pairs,
demonstrating the effectiveness of the social relation graph. We also explore
the effect of the knowledge graph embedding methods, the information fusing
method, and the time interval in constructing the social relation graph, which
shows the effectiveness of our model.
