@inproceedings{95f9f6e5e4e74049aed55eab2f418d35,
title = "Detecting spammers on social networks based on a hybrid model",
abstract = "The prosperity of social networks provides users with convenient communication but also attracts a large number of spammers. To solve this problem, this paper combines supervised learning and unsupervised learning algorithms, and proposes a novel hybrid model based on OPTICS and SVM. First, we collected a dataset from Sina Weibo including 10,000 users and 134,188 messages; then extracted the content based features and user behavior based features from the dataset; afterwards, we applied the features into the hybrid model to establish the classification model. The experiment shows that the proposed approach is capable of detecting spammers effectively with 87.6% spammers and 94.7% legitimate users correctly classified.",
keywords = "hybrid model, social network, spammer",
author = "Guangxia Xu and Jin Qi and Deling Huang and Mahmoud Daneshmand",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th IEEE International Conference on Big Data, Big Data 2016 ; Conference date: 05-12-2016 Through 08-12-2016",
year = "2016",
doi = "10.1109/BigData.2016.7840960",
language = "English",
series = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
pages = "3062--3068",
editor = "Ronay Ak and George Karypis and Yinglong Xia and Hu, {Xiaohua Tony} and Yu, {Philip S.} and James Joshi and Lyle Ungar and Ling Liu and Aki-Hiro Sato and Toyotaro Suzumura and Sudarsan Rachuri and Rama Govindaraju and Weijia Xu",
booktitle = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
}