  Social media is considered a democratic space in which people connect and
interact with each other regardless of their gender, race, or any other
demographic factor. Despite numerous efforts that explore demographic factors
in social media, it is still unclear whether social media perpetuates old
inequalities from the offline world. In this paper, we attempt to identify
gender and race of Twitter users located in U.S. using advanced image
processing algorithms from Face++. Then, we investigate how different
demographic groups (i.e. male/female, Asian/Black/White) connect with other. We
quantify to what extent one group follow and interact with each other and the
extent to which these connections and interactions reflect in inequalities in
Twitter. Our analysis shows that users identified as White and male tend to
attain higher positions in Twitter, in terms of the number of followers and
number of times in user's lists. We hope our effort can stimulate the
development of new theories of demographic information in the online space.
