Social media is a major platform for opinion sharing. To better understand and exploit opinions on social media, we aim to classify users with opposite opinions on a topic for decision support. Rather than mining text content, we introduce a link-based classification model named Global Consistency Maximization (GCM) that partitions a social network into two classes of users with opposite opinions. Experiments on a Twitter dataset show that: (1) our global approach achieves higher accuracy than two baseline approaches; and (2) link-based classifiers are more robust to small training samples if selected properly.
