Weibo spammers’ identification algorithm based on Bayesian model
In order to distinguish the spammers efficiently,a classifier based on the behavior characteristics was established.By analyzing the previous research,the ratio of followers,total number of blog posts,the number of friends,comprehensive quality evaluation and favorites according to latest data set,t...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2017-01-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017006/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539503623241728 |
|---|---|
| author | Yan-mei ZHANG Ying-ying HUANG Shi-jie GAN Yi DING Zhi-long MA |
| author_facet | Yan-mei ZHANG Ying-ying HUANG Shi-jie GAN Yi DING Zhi-long MA |
| author_sort | Yan-mei ZHANG |
| collection | DOAJ |
| description | In order to distinguish the spammers efficiently,a classifier based on the behavior characteristics was established.By analyzing the previous research,the ratio of followers,total number of blog posts,the number of friends,comprehensive quality evaluation and favorites according to latest data set,the Weibo spammers’ identification algorithm was realized based on Bayesian model and genetic algorithm.The experiment result based on the real-time data of Sina Weibo verify that the Bayesian model recognition algorithm can ensure spammers recognition accuracy without sacrificing recognition rate of non-spammers,and the proposed threshold value matrix proposed optimization can significantly improve recognition accuracy navy. |
| format | Article |
| id | doaj-art-526439692509439682de02ac42e56cb5 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2017-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-526439692509439682de02ac42e56cb52025-01-14T07:11:25ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2017-01-0138445359706604Weibo spammers’ identification algorithm based on Bayesian modelYan-mei ZHANGYing-ying HUANGShi-jie GANYi DINGZhi-long MAIn order to distinguish the spammers efficiently,a classifier based on the behavior characteristics was established.By analyzing the previous research,the ratio of followers,total number of blog posts,the number of friends,comprehensive quality evaluation and favorites according to latest data set,the Weibo spammers’ identification algorithm was realized based on Bayesian model and genetic algorithm.The experiment result based on the real-time data of Sina Weibo verify that the Bayesian model recognition algorithm can ensure spammers recognition accuracy without sacrificing recognition rate of non-spammers,and the proposed threshold value matrix proposed optimization can significantly improve recognition accuracy navy.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017006/network spammerspammer identificationWeiboBayesian modelgenetic algorithm |
| spellingShingle | Yan-mei ZHANG Ying-ying HUANG Shi-jie GAN Yi DING Zhi-long MA Weibo spammers’ identification algorithm based on Bayesian model Tongxin xuebao network spammer spammer identification Bayesian model genetic algorithm |
| title | Weibo spammers’ identification algorithm based on Bayesian model |
| title_full | Weibo spammers’ identification algorithm based on Bayesian model |
| title_fullStr | Weibo spammers’ identification algorithm based on Bayesian model |
| title_full_unstemmed | Weibo spammers’ identification algorithm based on Bayesian model |
| title_short | Weibo spammers’ identification algorithm based on Bayesian model |
| title_sort | weibo spammers identification algorithm based on bayesian model |
| topic | network spammer spammer identification Bayesian model genetic algorithm |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017006/ |
| work_keys_str_mv | AT yanmeizhang weibospammersidentificationalgorithmbasedonbayesianmodel AT yingyinghuang weibospammersidentificationalgorithmbasedonbayesianmodel AT shijiegan weibospammersidentificationalgorithmbasedonbayesianmodel AT yiding weibospammersidentificationalgorithmbasedonbayesianmodel AT zhilongma weibospammersidentificationalgorithmbasedonbayesianmodel |