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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 predicteddoi:10.18653/v1/d16-1105 dblp:conf/emnlp/EbrahimiDL16 fatcat:g7i34ftvsrallorgrlismmufl4