Most neural network models for document classification on social media focus
on text infor-mation to the neglect of other information on these platforms. In
this paper, we classify post stance on social media channels and develop UTCNN,
a neural network model that incorporates user tastes, topic tastes, and user
comments on posts. UTCNN not only works on social media texts, but also
analyzes texts in forums and message boards. Experiments performed on Chinese
Facebook data and English online debate forum data show that UTCNN achieves a
0.755 macro-average f-score for supportive, neutral, and unsupportive stance
classes on Facebook data, which is significantly better than models in which
either user, topic, or comment information is withheld. This model design
greatly mitigates the lack of data for the minor class without the use of
oversampling. In addition, UTCNN yields a 0.842 accuracy on English online
debate forum data, which also significantly outperforms results from previous
work as well as other deep learning models, showing that UTCNN performs well
regardless of language or platform.

本研究提出了一种称为 UTCNN 的神经网络模型，它结合了用户品味、主题品味和用户评论等非文本信息，可以在社交媒体、论坛和消息板中用于推文立场的分类。实验表明，UTCNN 的性能比其他模型和深度学习模型都要好，可以很好地解决类别不均衡问题。