  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 aspect. Despite numerous efforts that explore demographic aspects
in social media, it is still unclear whether social media perpetuates old
inequalities from the offline world. In this dissertation, we attempt to
identify gender and race of Twitter users located in the United States using
advanced image processing algorithms from Face++. We investigate how different
demographic groups connect with each other and differentiate them regarding
linguistic styles and also their interests. We quantify to what extent one
group follows and interacts with each other and the extent to which these
connections and interactions reflect in inequalities in Twitter. We also
extract linguistic features from six categories (affective attributes,
cognitive attributes, lexical density and awareness, temporal references,
social and personal concerns, and interpersonal focus) in order to identify the
similarities and the differences in the messages they share in Twitter.
Furthermore, we extract the absolute ranking difference of top phrases between
demographic groups. As a dimension of diversity, we use the topics of interest
that we retrieve from each user. Our analysis shows that users identified as
white and male tend to attain higher positions, in terms of the number of
followers and number of times in another user's lists, in Twitter. There are
clear differences in the way of writing across different demographic groups in
both gender and race domains as well as in the topic of interest. We hope our
effort can stimulate the development of new theories of demographic information
in the online space. Finally, we developed a Web-based system that leverages
the demographic aspects of users to provide transparency to the Twitter
trending topics system.
