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UTCNN: a Deep Learning Model of Stance Classification on Social Media Text

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Figure

Figure 1: Document composition in a convolutional neural network with three convolutional filters anduser- and topic-dependent semantic transformations
Figure 2: The UTCNN model. Assuming one post author, lembedding for topicsimplicity we do not explicitly plot the topic vector embedding part for comments, but it does include athe moderator matrix and vector embedding for moderator likers and p topics, x
Table 1: Annotation results of FBFans and CreateDebate dataset.
Table 3: Performance of post stance classification on the FBFans dataset.*UTCNN (full) results are statistically significant (p -value <0.005) with respect to all other methods except for UTCNN shareduser embedding.
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