  This study proposes content and interaction analysis techniques for a large
repository created from social media content. Though we have presented our
study for a large platform dedicated to discussions around financial topics,
the proposed methods are generic and applicable to all platforms. Along with an
extension of topic extraction method using Latent Dirichlet Allocation, we
propose a few measures to assess user participation, influence and topic
affinities specifically. Our study also maps user-generated content to
components of behavioral finance. While these types of information are usually
gathered through surveys, it is obvious that large scale data analysis from
social media can reveal many potentially unknown or rare insights.
Characterising users based on their platform behavior to provide critical
insights about how communities are formed and trust is established in these
platforms using graphical analysis is also studied.
