Abstract:
We are now a machine-mediated society, with virtually every transaction and decisionmediated, analyzed, and even recommended by algorithms running in the background. An
increasing percentage of our interactions and societal discourse now happen over social media platforms. While this has increased our connectivity, transcending geopolitical borders, it has also provided us with unique problems that we would not have anticipated even a decade ago. The propagation of misinformation on social networks is now a societal problem
and is prompting an increasing body of research into how to identify misinformation, how
to identify spreaders of such information and, perhaps more importantly, how to design
mitigation and intervention strategies. In this dissertation we show that advancing research
to address these challenges requires foundational research into automated user behavioral profiles. We present an unsupervised learning algorithm that leverages user-generated con-
tent to understand and categorize user behavior in near-real time. This line of work rests
fundamentally on the ability of machines to understand language. This, in itself, is a multi-
faceted challenge that has spawned entire domains in computer science, such as natural
language processing. To advance the ability of machines to understand content, we focus
on another fundamental problem, emotion recognition from short text in platforms, such as
Twitter and Reddit. We significantly advance this line of research by going beyond binary
sentiments and present sophisticated deep neural network-based models that can capture
fine-grained emotions in the presence of a host of challenges, including data imbalance and
noise due to human annotations. Finally, we ask a fundamental question of whether text
is sufficient to understand emotions and show that supplementing text with new modes of
interaction, such as emojis and emoticons, advances the ability of machines to disambiguate
emotions, much like humans. We believe that the research presented in this dissertation
lays the groundwork for further advancing machine understanding of human behavior in
social media platforms.
