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Hailu Thesis Final.pdf (2.09 MB)

ETD Abstract Container

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Efficient Spam Detection across Online Social Networks

Xu, Hailu
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658

Abstract Details

2016, Master of Science, University of Toledo, Engineering (Computer Science).
Online Social Networks (OSNs) have become more and more popular in the whole world recently. People share their personal activities, views, and opinions among different OSNs. Simultaneously, social spam appears more frequently and in various formats throughout popular OSNs. As big data theory receives much more attention, it is expected that OSNs will have more interactions with each other shortly. This would enable a spam link, content or profile attack to easily move from one social network like Twitter to other social networks like Facebook. Therefore, efficient detection of spam has become a significant and popular problem. This paper focuses on spam detection across multiple OSNs by leveraging the knowledge of detecting similar spam within an OSN and using it in different OSNs. We chose Facebook and Twitter for our study targets, considering that they share the most similar features in posts, topics, and user activities, etc. We collected two datasets from them and performed analysis based on our proposed methodology. The results show that detection combined with spam in Facebook show a more than 50% decrease of spam tweets in Twitter, and detection combined with spam of Twitter shows a nearly 71.2% decrease of spam posts in Facebook. This means similar spam of one social network can significantly facilitate spam detection in other social networks. We proposed a new perspective of spam detection in OSNs.
Weiqing Sun (Committee Chair)
Ahmad Javaid (Committee Co-Chair)
Hong Wang (Committee Member)
60 p.
Computer Science
spam detection, online social network, data analysis

Recommended Citations

Citations

  • Xu, H. (2016). Efficient Spam Detection across Online Social Networks [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658

    APA Style (7th edition)

  • Xu, Hailu. Efficient Spam Detection across Online Social Networks. 2016. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658.

    MLA Style (8th edition)

  • Xu, Hailu. "Efficient Spam Detection across Online Social Networks." Master's thesis, University of Toledo, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658

    Chicago Manual of Style (17th edition)

toledo1470416658
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© 2016, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.