Predicting Personal Opinion on Future Events with Fingerprints

Predicting users{'} opinions in their response to social events has important real-world applications, many of which political and social impacts. Existing approaches derive a population{'}s opinion on a going event from large scores of user generated content. In certain scenarios, we may not be able to acquire such content and thus cannot infer an unbiased opinion on those emerging events. To address this problem, we propose to explore opinion on unseen articles based on one{'}s fingerprinting: the prior reading and commenting history. This work presents a focused study on modeling and leveraging fingerprinting techniques to predict a user{'}s future opinion. We introduce a recurrent neural network based model that integrates fingerprinting. We collect a large dataset that consists of event-comment pairs from six news websites. We evaluate the proposed model on this dataset. The results show substantial performance gains demonstrating the effectiveness of our approach.