  Computational Social Science (CSS), aiming at utilizing computational methods
to address social science problems, is a recent emerging and fast-developing
field. The study of CSS is data-driven and significantly benefits from the
availability of online user-generated contents and social networks, which
contain rich text and network data for investigation. However, these
large-scale and multi-modal data also present researchers with a great
challenge: how to represent data effectively to mine the meanings we want in
CSS? To explore the answer, we give a thorough review of data representations
in CSS for both text and network. Specifically, we summarize existing
representations into two schemes, namely symbol-based and embedding-based
representations, and introduce a series of typical methods for each scheme.
Afterwards, we present the applications of the above representations based on
the investigation of more than 400 research articles from 6 top venues involved
with CSS. From the statistics of these applications, we unearth the strength of
each kind of representations and discover the tendency that embedding-based
representations are emerging and obtaining increasing attention over the last
decade. Finally, we discuss several key challenges and open issues for future
directions. This survey aims to provide a deeper understanding and more
advisable applications of data representations for CSS researchers.
