Earticle

초록

영어

Microblogging, also known as Weibo, has currently become the prevalent social networking platform, and its function of disseminating information has been increasingly recognized and valued. One of the main approaches of information communication on Weibo is forwarding activities among users. Therefore, the key interests have focused on the microblogs that are easily recognized and forwarded. Through predicting the possibility of a micro-blog being forwarded among users, it is able to effectively improve the efficiency of information spreading on the Weibo platform. On the basis of Sina Weibo data, this paper intends to study 13 features of Sina Weibo including user and content, so as to conduct machine learning, to establish a feature analysis model, to identify key influence factors on Weibo forwarding, to study the combination of feature attributes for the first time, to explore the correlation among features, then to predict the probability of a micro-blog being forwarded by users. Meanwhile, concerning different features held by different data, different machine learning algorithms are applied in model training, in order to identify the algorithm with the highest prediction accuracy.

목차

Abstract
 1. Introduction
 2. Literature Review
 3. Study Content
  3.1. Data Processing
  3.2 Evaluation Index of Text Classification
  3.2. Experiment Description and Results Analysis
 4. Conclusion
 References

키워드

저자

• Lufei Wang [ Beijing Jiaotong University, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605