Collaborative tagging and
social search are among
the most successful
social media and “wisdom of
crowds” applications in which users
annotate webpages or other resources
using tags. Such tags are
shared among users and can be explored
to enable a range of information
retrieval and recommendation
capabilities. One major obstacle hindering
the adoption of tagging-based
systems or services is the presence
of noises and ambiguities in userprovided
tags. In their article “Modeling
Social Annotations via Latent
Reason Identification,” Xiance Si,
Zhiyuan Liu, and Maosong Sun propose
the Tag Allocation Model to
tackle this challenge. They show their
generative model delivers good performance
in tag recommendations
and tag-hierarchy discovery.
Three-dimensional virtual worlds
such as Second Life have drawn a lot
of attention from research communities
and the industry. User-generated
social media content through avatars
abounds in virtual worlds. Yet, systematic
data collection from this relatively
new social media channel and
behavioral analysis of avatars have
been underexplored. Yulei Zhang,
Ximing Yu, Yan Dang, and Hsinchun
Chen’s article “An Integrated Framework
for Avatar Data Collection
from the Virtual World” proposes an
integrated approach that combines
bot- and spider-based techniques to
collect avatar behavioral and profile
data. They also report empirical findings
examining differences in avatar
behavior based on avatar gender and
age
“Using Social Media to Predict
Future Events with Agent-Based
Markets” by Efthimios Bothos,
Dimitris Apostolou, and Gregoris
Mentzas proposes a prediction
market approach using computational
agents as opposed to human
participants. Such agents embody
human-user sentiments—as well as
their knowledge, beliefs, and assessments,
all extracted from social media—
and participate in prediction
markets to predict future events.
This type of automated approach
could potentially overcome implementation
difficulties associated
with standard “wisdom of crowds”
approaches such as prediction markets
with human participants.