A Semi-Supervised Framework for Social Spammer Detection-Reference-Cited by-同舟云学术

A Semi-Supervised Framework for Social Spammer Detection

Published:2015 Issue: Volume: Page:177-188
ISSN:0302-9743
Container-title:Advances in Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Li Zhaoxing,Zhang Xianchao,Shen Hua,Liang Wenxin,He Zengyou

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-18032-8_14

Reference21 articles.

1. Amleshwaram, A.A., Reddy, N., Yadav, S., Gu, G., Yang, C.: Cats: characterizing automation of twitter spammers. In: 2013 Fifth International Conference on Communication Systems and Networks (COMSNETS), pp. 1–10. IEEE (2013)

2. Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In: Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS) (2010)

3. Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., Gonçalves, M.: Detecting spammers and content promoters in online video social networks. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR), pp. 620–627. ACM (2009)

4. Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.: Detecting and characterizing social spam campaigns. In: Proceedings of the 10th Annual Conference on Internet Measurement(IMC), pp. 35–47. ACM (2010)

5. Ghosh, S., Viswanath, B., Kooti, F., Sharma, N.K., Korlam, G., Benevenuto, F., Ganguly, N., Gummadi, K.P.: Understanding and combating link farming in the twitter social network. In: Proceedings of the 21st International Conference on World Wide Web, pp. 61–70. ACM (2012)

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