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

Abstract

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 perday. 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 dissertationdevelops semi-supervised methods for stance mining.Most existing studies on stance mining take a simplistic view that assumes a sentence (like a Tweet) holds a perspective that is independent of the context and the author’s network position. This approach to stance learning leaves three crucial unresolved challenges. First, how do we train stance-learning models on new topics with minimal labeling effort? Discussion topics change fast and new issues emerge, making it difficult to reuse prior labeled data. However, artifacts of social networks like hashtags can give noisy signal about the stance of users. To extract the signal from noise, I develop methods to find useful hashtags by exploiting how users in the pro-group and the anti-group u…