Social Media Analytics for Stance Mining A Multi-Modal Approach with Weak Supervision

People express their opinions on blogs and other social media platforms. As per a recent estimate, interactions on Twitter alone result in over 500 million tweets per day. The magnitude of this data enables new applications of opinion mining that have previously remained challenging e.g., finding users’ stance (as in pro or con) on topics of interest. However, one of the major barriers to utilizing this amount of data is the cost of hand-labeling examples for machine learning. This barrier is even more apparent in stance mining, as opinions can change overtime and can be about any issues. To reduce the need for hand-labeled data by taking the complex interactions of social media users and their social influence into account, this dissertation develops semi-supervised methods for stance mining.