ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (6): 1282-1293.doi: 10.7544/issn1000-1239.2018.20161057

• 人工智能 • 上一篇    下一篇

基于传播加速度的微博流行度预测方法

朱海龙1,2,3,云晓春1,2,3,韩志帅4   

  1. 1(中国科学院信息工程研究所 北京 100093); 2(中国科学院大学网络空间安全学院 北京 100049); 3(国家计算机网络应急技术处理协调中心 北京 100029); 4(北京科技大学计算机与通信工程学院 北京 100083) (zhl@cert.org.cn)
  • 出版日期: 2018-06-01
  • 基金资助: 
    国家自然科学基金项目(61572496)

Weibo Popularity Prediction Method Based on Propagation Acceleration

Zhu Hailong1,2,3, Yun Xiaochun1,2,3, Han Zhishuai4   

  1. 1(Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093); 2(School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049); 3(National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029); 4(School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083)
  • Online: 2018-06-01

摘要: 微博流行度预测是根据微博早期的传播特征来预测其未来的传播范围.目前的主要方法是根据信息早期传播的流行度进行预测,忽略了传播速度变化的趋势,这导致此类方法在预测微博消息未来流行度时准确性较差.为了更准确和方便地预测微博未来流行度,提出了一个多元线性回归模型:用户活跃度及传播加速度(user activity propagation acceleration, UAPA)模型.首先,研究了未来流行度与早期传播趋势变化的联系,发现两者存在正相关关系,根据这个发现,提出了传播加速度的概念,并基于传播加速度和早期流行度建立了预测模型.然后,分析了微博用户周期性的活动现象并发现用户转发数量在一天的不同时刻差异很大,传播加速度和流行度也不同.基于这种情况,根据用户活跃性优化了预测模型.最后在2个真实数据集(分别有100万和41万条微博)上对比了UAPA模型与业内代表性流行度预测方法的预测准确度,分析了模型中参数取值对于预测效果的影响.实验表明:提出的UAPA模型在多个性能指标上都优于现有方法.

关键词: 微博, 流行度, 预测, 用户活跃度, 传播加速度

Abstract: Weibo popularity prediction attempts to forecast the future diffusion range of Weibo messages based on the propagation features at early stages. The existing methods are mainly depended on messages’ early popularity, ignoring the propagation trend at that time, which leads to poor predicting accuracy when these methods applied on Weibo messages. For the purpose of forecasting the Weibo popularity more accurately and conveniently, we propose a multiple linear regression model: UAPA (user activity propagation acceleration) in this paper. Firstly, we investigate the relationship between future popularity and varying trend of Weibo diffusion, and find that they are positive correlation. Based on this detection, we present the concept of propagation acceleration which describes the spreading varying speed of Weibo, then we build a predicting model based on propagation acceleration and popularity at early stages. Furthermore, we analyze the Weibo user periodic activity and find that the retweeting times of users vary greatly at different time in one day, and the messages’ popularity and propagation acceleration are also distinct at various moments. In the light of this finding, we optimize the predicting model by user activity. Finally, we compare prediction accuracy of UAPA model mentioned above with representative popularity prediction methods on two real datasets, with 1000 thousands and 410 thousands messages respectively, and discuss the influence of parameter value in UAPA model on prediction performance. Experiments show that UAPA model is superior to the existing methods on multiple indicators.

Key words: Weibo, popularity, predication, user activity, propagation acceleration

中图分类号: