| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 01033 | |
| Number of page(s) | 4 | |
| Section | Network Security System, Neural Network and Data Information | |
| DOI | https://doi.org/10.1051/matecconf/201823201033 | |
| Published online | 19 November 2018 | |
Machine Users Detection on Sina Weibo Platform
1
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
2
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
3
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
a Corresponding author: 969006768@qq.com
In recent years, the rapid development of Sina Weibo has made it the representative of many Weibo platforms in China. Sina Weibo has attracted large numbers of users in China because of its fast speed of information dissemination, simple use and many star users. More and more Chinese people get news and share information through Sina Weibo. In addition to the normal users, Sina Weibo also appeared on some machine users, these users are generated in order to create false sentiment, which seriously affected the good order of the Sina Weibo platform. By studying normal users and machine users, this paper extracts eight features, they are the number of followings, the number of followers, the number of Weibos, the number of years using Sina Weibo, Sunshine credit, the number of Weibos you like, the proportion of following others by recommending and the ratio of followings and followers. Naive Bayes classification approach, KNN classification approach and SVC classification approach are used for experiment. The experimental results show that the recall rate of the machine users detection is above 96% and the accuracy rate is above 98%, which validates the validity of the features extracted in this paper.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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