Social Media Analytics
and Intelligence Research
Research on social media has greatly
intensified in the past few years given
the significant interest from the application’s
perspective and the associated
unique technical and social
science challenges and opportunities.
This research agenda is multidisciplinary
in nature and has drawn attention
from research communities in
all major disciplines. From an information
technology standpoint, social
media research has primarily focused
on social media analytics and, more
recently, social media intelligence.
Social media analytics is concerned
with developing and evaluating
informatics tools and frameworks to
collect, monitor, analyze, summarize,
and visualize social media data, usually
driven by specific requirements
from a target application. Social media
analytics research serves several
purposes:
• facilitating conversations and interaction
between online communities
and
• extracting useful patterns and intelligence
to serve entities that include,
but are not limited to, active
contributors in ongoing dialogues.
From a technical perspective, social
media analytics research faces
several unique challenges. First, social
media contains an enriched set
of data or metadata, which have
not been treated systematically in
data- and text-mining literature.
Examples include tags (annotations
or labels using free-form keywords);
user-expressed subjective
opinions, insights, evaluation, and
perspectives; ratings; user profiles;
and both explicit and implicit social
networks. Second, social media applications
are a prominent example
of human-centered computing with
their own unique emphasis on social
interactions among users. Hence,
issues such as context-dependent
user profiling and needs elicitation
as well as various kinds of humancomputer
interaction considerations
must be reexamined. Third, although
social media promises a new
approach to tackling the noise and
information-overload problem with
Web-based information processing,
issues such as semantic inconsistency,
conflicting evidence, lack
of structure, inaccuracies, and difficulty
in integrating different kinds
of signals abound in social media.
Fourth, social media data are dynamic
streams, with their volume