Hailu Xu

School of Computing and Information Sciences

Lecture Information:
  • June 25, 2020
  • 10:00 AM
  • Via Zoom: https://fiu.zoom.us/j/3308492811

Speaker Bio

Hailu Xu is currently a Ph.D. candidate in the School of Computing and Information Sciences (SCIS) at Florida International University (FIU). He is supervised by Dr. Liting Hu. His research interests lie in the area of system with a focus on the online social spam detection in social networks. Hailu obtained his M.Sc. degree in Computer Science from University of Toledo in 2016 and B.Sc. degree in Computer Science from North China Electric Power University in 2014. He interned at Lawrence Livermore National Laboratory in the summer of 2019. He received the Best Student Paper Award from Cloud 2019.


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 which were trained based on historical records. Therefore, these lead a question: how can we uncover and defend against these online spam activities in an online and scalable manner?

In this dissertation, we present three systems that support scalable and online social spam detection from streaming social data: (1) the first part introduces a scalable system that can support large-scale online social spam detection, (2) the second part introduces a system named SpamHunter, a novel system 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.