Topic: Cyberbullying Detection by Applying AI Approaches
Speakers: Prof. Chengqi Zhang and Dr. Guodong Long, University of Technology, Sydney
Organisers: The Canberra Data Scientists group and the Information Technology & Engineering Program, University of Canberra
Date and time: 4:30-6:00pm, Tuesday, 2 February 2016
Location: Teal Room, Inspire Centre (Building 25 on the map at http://www.canberra.edu.au/maps/campus-map)
RSVP URL: http://www.meetup.com/CanberraDataScientists/events/228252655/
Cyberbullying is the use of technology to bully a person or group with the intent to hurt them socially, psychologically or even physically. Currently there are many young people being cyberbullied or involving in cyberbullying activities, and the cyberbullying offence has been defined as criminal activity by law. To avoid the severe results (e.g. spirit trauma, or be charged as criminal), cyberbullying detection emerged to real-time proactively prevent cyberbullying by generating early warning. Most studies on Cyberbullying detection focus on key-words search and sentiment filtering on textual contents. All of them neglects the online conversation's rich context information including texts, networks, time and demographics. In this talk, we will introduce a novel solution for applying AI approaches to detect cyberbullying by exploiting rich heterogeneous context information.
Prof. Chengqi Zhang is a Research Professor of Information Technology at The University of Technology Sydney (UTS), an Honorary Professor of the University of Queensland (UQ), Director of the UTS Priority Research Centre for Quantum Computation & Intelligent Systems (QCIS). He is Alternative Dean of UTS Graduate Research School, Chairman of the Australian Computer Society National Committee for Artificial Intelligence and Chairman of IEEE Computer Society Technical Committee of Intelligent Informatics (TCII). Chengqi Zhang obtained his PhD degree from the University of Queensland in 1991, followed by a Doctor of Science (DSc – Higher Doctorate) from Deakin University in 2002. His key areas of research are Distributed Artificial Intelligence, Data Mining and its applications. He has published more than 200 refereed research papers and six monographs and edited 16 books. He has attracted 12 ARC grants of $4.7M. He is a Fellow of the Australian Computer Society (ACS) and a Senior Member of the IEEE Computer Society (IEEE).
Dr. Guodong Long has over 10 years experience on leading, developing and coordinating industry and research projects. Since joined UTS in 2010, he has practically led total five industry projects including three ARC Linkage projects. Before join in UTS in 2010, Dr Long has over 6 years industry work experience in IT company. He has strong system-wide knowledge of all computer-related, especially for architecture and design for artificial intelligent based systems; and have strong creativity on research methodology and real application systems. He currently leads a research team to conduct application-driven research by collaborating with industry partners. He obtained his BSc and MSc degree from National University of Defence Technology (NUDT) in 2002, 2008, and PhD degree from University of Technology Sydney (UTS) in 2014, all from computer science.