基于XGBoost的微博流行度预测算法

Microblog Popularity Prediction Algorithm Based on XGBoost

随着全媒体时代的到来和社交网络的发展,流行度预测在舆情监测和数据话语权的争夺上开始发挥重要的作用。现有的流行度预测研究多集中于外文媒体,对以微博为代表的国内主流媒体进行流行度预测是一个新兴且具有挑战的方向。本文针对微博这一国内社交媒体平台进行研究,通过对微博内容及微博用户的特征分析,设计了多种流行度预测方案,同时,提出了一种基于XGBoost的微博流行度预测算法,将流行度预测问题转换为互动值档位分类问题,在分类式框架下将提取融合后的特征用于模型训练,可以较为准确地对有用户信息的微博的流行度情况进行预测。本文的算法在微博流行度预测数据集中得到验证,并且取得了准确率高达85.69%的优越效果。

With the advent of the all-media era and the development of social networks,the popularity prediction begins to play an important role in the monitoring of public opinion and the competition of data discourse power.The existing popularity prediction researches mostly focuse on foreign media,and it is an emerging and challenging direction to predict the popularity of domestic mainstream media such as microblog.In this paper,we conduct the research on microblog,a domestic social media platform,through the analysis of microblog’s content and users,and design a variety of popularity prediction schemes.Meanwhile,we propose a microblog popularity prediction algorithm based on XGBoost,which converts the popluarity prediction problem into an interactive value file classification problem,and use the extracted and fused features for model training under the categorical framework,which can predict the popularity of microblog with user information more accurately.The proposed algorithm is verified in the microblog popularity prediction dataset,whose accuracy rate can achieve as high as 85.69%.