Doctor of Philosophy (PhD)
First Advisor's Name
First Advisor's Committee Title
Second Advisor's Name
S. S. Iyengar
Second Advisor's Committee Title
Third Advisor's Name
Third Advisor's Committee Title
Fourth Advisor's Name
Fourth Advisor's Committee Title
Fifth Advisor's Name
Fifth Advisor's Committee Title
Social spam detection, scalable system, stream system, online social network
Date of Defense
The broad success of online social networks (OSNs) has created fertile soil for the emergence and fast spread of social spam. Fake news, malicious URL links, fraudulent advertisements, fake reviews, and biased propaganda are bringing serious consequences for both virtual social networks and human life in the real world. Effectively detecting social spam is a hot topic in both academia and industry. However, traditional social spam detection techniques are limited to centralized processing on top of one specific data source but ignore the social spam correlations of distributed data sources. Moreover, a few research efforts are conducting in integrating the stream system (e.g., Storm, Spark) with the large-scale social spam detection, but they typically ignore the specific details in managing and recovering interim states during the social stream data processing. We observed that social spammers who aim to advertise their products or post victim links are more frequently spreading malicious posts during a very short period of time. They are quite smart to adapt themselves to old models that were trained based on historical records. Therefore, these bring a question: how can we uncover and defend against these online spam activities in an online and scalable manner? In this dissertation, we present there systems that support scalable and online social spam detection from streaming social data: (1) the first part introduces
Oases, a scalable system that can support large-scale online social spam detection, (2) the second part introduces a system named SpamHunter, a novel system that supports efficient online scalable spam detection in social networks. The system gives novel insights in guaranteeing the efficiency of the modern stream applications by leveraging the spam correlations at scale, and (3) the third part refers to the state recovery during social spam detection, it introduces a customizable state recovery framework that provides fast and scalable state recovery mechanisms for protecting large distributed states in social spam detection applications.
Previously Published In
Xu, Hailu, Liting Hu, Pinchao Liu, and Boyuan Guan. "Exploiting the Spam Correlations in Scalable Online Social Spam Detection." In International Conference on Cloud Computing, pp. 146-160. Springer, Cham, 2019.
Xu, Hailu, Liting Hu, Pinchao Liu, Yao Xiao, Wentao Wang, Jai Dayal, Qingyang Wang, and Yuzhe Tang. "Oases: an online scalable spam detection system for social networks." In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 98-105. IEEE, 2018.
Xu, Hailu, Boyuan Guan, Pinchao Liu, William Escudero, and Liting Hu. "Harnessing the nature of spam in scalable online social spam detection." In 2018 IEEE International Conference on Big Data (Big Data), pp. 3733-3736. IEEE, 2018.
Xu, Hailu, "Support Efficient, Scalable, and Online Social Spam Detection in System" (2020). FIU Electronic Theses and Dissertations. 4499.
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