Weakly Supervised Tweet Stance Classification by Relational Bootstrapping

Javid Ebrahimi, Dejing Dou, Daniel Lowd
2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing  
Supervised stance classification, in such domains as Congressional debates and online forums, has been a topic of interest in the past decade. Approaches have evolved from text classification to structured output prediction, including collective classification and sequence labeling. In this work, we investigate collective classification of stances on Twitter, using hinge-loss Markov random fields (HL-MRFs). Given the graph of all posts, users, and their relationships, we constrain the predicted
more » ... train the predicted post labels and latent user labels to correspond with the network structure. We focus on a weakly supervised setting, in which only a small set of hashtags or phrases is labeled. Using our relational approach, we are able to go beyond the stance-indicative patterns and harvest more stance-indicative tweets, which can also be used to train any linear text classifier when the network structure is not available or is costly.
doi:10.18653/v1/d16-1105 dblp:conf/emnlp/EbrahimiDL16 fatcat:g7i34ftvsrallorgrlismmufl4