A key challenge in social network analysis is understanding the position, or
stance, of people in the graph on a large set of topics. While past work has
modeled (dis)agreement in social networks using signed graphs, these approaches
have not modeled agreement patterns across a range of correlated topics. For
instance, disagreement on one topic may make disagreement(or agreement) more
likely for related topics. We propose the Stance Embeddings Model(SEM), which
jointly learns embeddings for each user and topic in signed social graphs with
distinct edge types for each topic. By jointly learning user and topic
embeddings, SEM is able to perform cold-start topic stance detection,
predicting the stance of a user on topics for which we have not observed their
engagement. We demonstrate the effectiveness of SEM using two large-scale
Twitter signed graph datasets we open-source. One dataset, TwitterSG, labels
(dis)agreements using engagements between users via tweets to derive
topic-informed, signed edges. The other, BirdwatchSG, leverages community
reports on misinformation and misleading content. On TwitterSG and BirdwatchSG,
SEM shows a 39% and 26% error reduction respectively against strong baselines.

该论文提出了一种基于 Stance Embedding 模型的社交网络分析方法，该模型能够识别用户在不同话题上的立场并将其通过 signed social graphs 表达，同时还能在缺少用户参与数据的情况下进行观点预测，作者使用 Twitter 数据集对该模型进行了验证，证明了 SEM 在话题分析方面的有效性。