A Stochastic Attention CNN Model for Rumor Stance Classification

Abstract

Rumor stance classification is a task for identifying different stances about specific social media posts, and it is considered as an important step to prevent rumors from spreading. However, most previous studies for the rumor stance classification, which focus on content features of posts, ignore the textual information granularity and reading (or writing) habits of the public. In social networks, people may have different stances towards unverified posts with different reading habits. Based on this observation, we propose a Stochastic Attention Convolutional Neural Net (SACNN) under different textual information granularity to catch different habits of the public for the rumor stance classification task. Specifically, being treated as view-windows of readers, convolutional kernels in a SACNN contain trainable convolutional kernels and stochastic untrainable convolutional kernels, and different sizes of the stochastic convolutional kernels are used to simulate casual online reading habits and to extract course-grained features such as phrase features. After the convolutional layer, fine-grained features such as keywords can be extracted by a pooling layer. The experiments show that the average accuracy and F1 score of our proposed model are respectively 0.26% and 0.46% higher than the state-of-the-art results on the rumor stance classification task on PHEME dataset.