Weitere Kapitel dieses Buchs durch Wischen aufrufen
In the present situation, the issue of identifying spammers has received increasing attention because of its practical relevance in the field of social network analysis. The growing popularity of social networking sites has made them prime targets for spammers. By allowing users to publicize and share their independently generated content, online social networks become susceptible to different types of malicious and opportunistic user actions. Social network community users are fed with irrelevant information while surfing, due to spammer’s activity. Spam pervades any information system such as email or Web, social, blog, or reviews platform. Therefore, this paper attempts to review various spam detection frameworks that which deal about the detection and elimination of spams in various sources.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Danah michele boyd, “ Friendster and publicly articulated social networking”, proceedings of Conference on Human Factors and Computing Systems (CHI 2004), pp. 1279–1282, 2004.
Markus Jakobsson, Jacob Ratkiewicz, “ Designing ethical phishing experiments: a study of (ROT13) rOnl query features”, Proceedings of the 15th international conference on World Wide Web (2006), pp. 513–522, 2006.
Alex Tsow and Markus Jakobsson, Deceit and Deception: A Large User Study of Phishing, Technical Report TR649, Indiana University, Bloomington, August 2007.
Takeda, T.; Takasu, A., “ A splog filtering method based on string copy detection”, proceedings of First International Conference on Applications of Digital Information and Web Technologies, pp. 543–548, 2008.
Kamaliha, E.; Riahi, F.; Qazvinian, V.; Adibi, J., “ Characterizing Network Motifs to Identify Spam Comments”, proceedings of IEEE International Conference on Data Mining Workshops, pp. 919–928, 2008.
Webb, Steve, Caverlee, James and Pu, Calton, Social Honeypots: “Making Friends With A Spammer Near You”, Paper presented at the meeting of the CEAS, 2008.
Gianluca Stringhini, Christopher Kruegel, Giovanni Vigna, “ Detecting Spammers on Social Networks”, proceedings of Annual Computer Security Applications Conference (ACSAC) 2010.
Kyumin Lee, James Caverlee, Steve Webb, “ Uncovering Social Spammers: Social Honeypots + Machine Learning”, proceedings of ACM-SIGIR 2010.
Sreenivasan, Shrijina, Lakshmipathi, B., “ An Unsupervised Model to detect Web Spam based on Qualified Link Analysis and Language Models”, International Journal of Computer Applications, vol. 63, issue 4, pp. 33–37, 2013.
K. Karthick, V. Sathiya, J. Pugalendiran, “ Detecting Nepotistic Links Based On Qualified Link Analysis and Language Models”, International Journal of Computer Trends and Technology, pp. 106–109, June 2011.
Qureshi, M.A.; Younus, A.; Touheed, N.; Qureshi, M.S.; Saeed, M., “ Discovering Irrelevance in the Blogosphere through Blog Search”, proceedings of International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 457–460, 2011.
Jenq-Haur Wang; Ming-Sheng Lin, “ Using Inter- comment Similarity for Comment Spam Detection in Chinese Blogs”, proceedings of International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 189–194, 2011.
Archana Bhattarai, Vasile Rus, and Dipankar Dasgupta, “ Characterizing Comment Spam in the Blogosphere through Content Analysis”, proceedings of IEEE Symposium on Computational Intelligence in Cyber Security—CICS, pp. 37–44, 2009.
Sakakura, Y.; Amagasa, T.; Kitagawa, H., “ Detecting Social Bookmark Spams Using Multiple User Accounts”, proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1153–1158, 2012.
Yang Yu; Yuzhong Chen, “ A novel content based and social network aided online spam short message filter”, proceedings of 10th World Congress on Intelligent Control and Automation (WCICA), pp. 444–449, 2012.
Ravindran, P.P.; Mishra, A.; Kesavan, P.; Mohanavalli, S., “ Randomized tag recommendation in social networks and classification of spam posts”, proceedings of IEEE International Workshop on Business Applications of Social Network Analysis (BASNA), pp. 1–6, 2010.
Ariaeinejad, R.; Sadeghian, A., “ Spam detection system: A new approach based on interval type- 2 fuzzy sets”, proceedings of 24th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 379–384, 2011.
Ishida, K., “ Mutual detection between spam blogs and keywords based on cooccurrence cluster seed”, proceedings of First International Conference on Networked Digital Technologies, pp. 8–13, 2009.
- A Study of Spam Detection Algorithm on Social Media Networks
Jacob Soman Saini
- Springer India
Neuer Inhalt/© ITandMEDIA, Best Practices für die Mitarbeiter-Partizipation in der Produktentwicklung/© astrosystem | stock.adobe.com