Spam detection on social networks using cost-sensitive feature selection and ensemble-based regularized deep neural networks-Reference-Cited by-同舟云学术

Spam detection on social networks using cost-sensitive feature selection and ensemble-based regularized deep neural networks

Published:2019-07-02 Issue:9 Volume:32 Page:4239-4257
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Barushka Aliaksandr,Hajek Petr^ORCID

Funder

Grantová Agentura České Republiky

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-019-04331-5.pdf

Reference71 articles.

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2. Nexgate (2013) State of social media spam. http://nexgate.com/wp-content/uploads/2013/09/Nexgate-2013-State-of-Social-Media-Spam-Research-Report.pdf . Accessed 20 Apr 2019

3. Statista (2018) Twitter: number of monthly active users 2010–2018. https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/ . Accessed 20 Apr 2019

4. Prieto VM, Alvarez M, Cacheda F (2013) Detecting linkedin spammers and its spam nets. Int J Adv Comput Sci Appl (IJACSA) 4(9):189–199

5. Shen H, Ma F, Zhang X, Zong L, Liu X, Liang W (2017) Discovering social spammers from multiple views. Neurocomputing 225:49–57. https://doi.org/10.1016/j.neucom.2016.11.013

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