Learning Stance Embeddings from Signed Social Graphs
This work addresses the challenge of understanding user positions on topics in social networks, which is important for applications like misinformation detection, but it is incremental as it builds on existing signed graph methods by incorporating topic correlations.
The paper tackles the problem of modeling user stances across multiple correlated topics in signed social graphs, proposing the Stance Embeddings Model (SEM) that jointly learns user and topic embeddings to enable cold-start topic stance detection, resulting in error reductions of 39% and 26% on two Twitter datasets.
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.