1. A
COMPREHENSIVE STUDY ON SOCIAL NETWORK MENTAL DISORDERS DETECTION VIA ONLINE
SOCIAL MEDIA MINING
Abstract:
The
explosive growth in popularity of social networking leads to the problematic
usage. An increasing number of social network mental disorders (SNMDs), such as
Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have
been recently noted. Symptoms of these mental disorders are usually observed
passively today, resulting in delayed clinical intervention. In this paper, we
argue that mining online social behavior provides an opportunity to actively
identify SNMDs at an early stage. It is challenging to detect SNMDs because the
mental status cannot be directly observed from online social activity logs. Our
approach, new and innovative to the practice of SNMD detection, does not rely
on self-revealing of those mental factors via questionnaires in Psychology.
Instead, we propose a machine learning framework, namely, Social Network
Mental Disorder Detection (SNMDD), that exploits features extracted from
social network data to accurately identify potential cases of SNMDs. We also
exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor
Model (STM) to improve the accuracy. To increase the scalability of STM, we
further improve the efficiency with performance guarantee. Our framework is
evaluated via a user study with 3126 online social network users. We conduct a
feature analysis, and also apply SNMDD on large-scale datasets and analyze the
characteristics of the three SNMD types. The results manifest that SNMDD is
promising for identifying online social network users with potential SNMDs.
2. A
NOVEL REPRESENTATION AND COMPRESSION FOR QUERIES ON TRAJECTORIES IN ROAD
NETWORKS
Abstract:
Recording
and querying time-stamped trajectories incurs high cost of data storage and
computing. In this paper, we explore several characteristics of the
trajectories in road networks, which have motivated the idea of coding
trajectories by associating timestamps with relative spatial path and
locations. Such a representation contains large number of duplicate information
to achieve a lower entropy compared with the existing representations, thereby
drastically cutting the storage cost. We propose several techniques to compress
spatial path and locations separately, which can support fast positioning and
achieve better compression ratio. For locations, we propose two novel encoding
schemes such that the binary code can preserve distance information, which is
very helpful for LBS applications. In addition, an unresolved question in this
area is whether it is possible to perform search directly on the compressed
trajectories, and if the answer is yes, then how. Here we show that directly
querying compressed trajectories based on our encoding scheme is possible and
can be done efficiently. We design a set of primitive operations for this
purpose, and propose index structures to reduce query response time. We
demonstrate the advantage of our method and compare it against existing ones
through a thorough experimental study on real trajectories in road network.
