ABSTRACT:
The wide spread use of social network services, especially location
based services, has transformed social networks into an important information
source of real-world events. Many event detection systems using geo-tagged
posts from social networks have been developed in recent years. Besides
detecting real-world events, it is also desirable for government officials,
news media, and police etc. to identify on-site users of an event, from whom we
could gather valuable information regarding the process of events and
investigate suspects when an event is associated with crime or terrorist.
However, due to the high uncertainty of human mobility patterns and the low
probability of users sharing their location information, it is difficult to
identify on-site users while a social event unfolds, and research work in this
area is still in its infancy. In this paper, we propose a Fused
fEature Gaussian prOcess
Regression (FEGOR) model, which exploits three influential factors in social
networks for on-site user identification: mobility influence, content
similarity, and social relationship. By integrating these factors, we are able
to estimate the distance between a user and a social
event even when the user’s location profile is unknown, thus identify on-site
users. Experiments on a real-world Twitter dataset demonstrate the
effectiveness of our model, achieving a minimum mean absolute error of 1.7km
and outperforming state-of-the-art methods.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
·
System : Pentium Dual Core.
·
Hard Disk : 120 GB.
·
Monitor :
15’’ LED
·
Input
Devices : Keyboard, Mouse
·
Ram : 1
GB
SOFTWARE
REQUIREMENTS:
·
Operating
system : Windows 7.
·
Coding
Language : JAVA/J2EE
·
Tool : Netbeans 7.2.1
·
Database
: MYSQL
REFERENCE:
Zhiwen Yu, Senior Member, IEEE, Fei Yi, Qin Lv, Bin Guo, Senior Member, IEEE, “Identifying On-site Users for
Social Events: Mobility, Content, and Social Relationship”, IEEE Transactions
on Mobile Computing, 2018.