Correlation identification in multimodal weibo via back propagation neural network with genetic algorithm

Abstract

The rapid development of social media services has spawned abundant user generated contents (UGC), such as Sina Weibo, which is one of the biggest Chinese microblogging platforms. In order to enhance the quality and popularity of the posted weibo (the microblog), Weibo users usually embed some social information and images or micro-videos, namely the multimodal weibo, and we assume that there is a close correlation among the multimodal weibo data, especially between the visual data (image/micro-video) and its corresponding text, for a multimodal weibo of high quality. Hence, we try to evaluate the quality of multimodal weibo via analyzing the correlation in the multimodal weibo. This paper constructs the classification model based on back propagation (BP) neural network with genetic algorithm (GA), to automatically identify the correlation within the multimodal weibo, and investigates three kinds of features from multimodal weibo to uncover their contributions to the correlation. The experimental results verify the superiority of the GA-BP based classification model over the traditional BP neural network.