Social Media User's Activity Classification Integrating Image and Spatiotemporal Information

  Objectives  Social media check-in data contains a large number of social media users' activity records. It is important to understand social media users' activities for exploring human mobility and behavioral patterns.

  Methods  We present a user activity classification method on the check-in data of Sina Weibo, which is a very popular Chinese social network service, referred to as Weibo. This method achieves the goal of recognizing users' activities by combining image expression and spatiotemporal data classification technology on the Weibo check-in data. Firstly, we divide Weibo user activities into six categories, including catering, social service, education, outdoor, entertainment, and travel related, according to the points of interest attribution of check-in. Secondly, by applying the convolutional neural network and K-nearest neighbor method, the scene information in images and spatiotemporal information in check-ins are merged to classify the activity behavior of Weibo users.

  Results  Experimental results show that the proposed method significantly improves the accuracy of Weibo user activity classification.

  Conclusions  Although the classification method cannot comprehensively improve the performance of all user activity types, it can better express the microblog user activity and provide more effective data support for the exploration of human behavior.