  Conversations are an integral part of online social media, and gaining
insights into these conversations is of significant value for many commercial
as well as academic use cases. From a computational perspective, however,
analyzing conversation data is complex, and numerous aspects must be
considered. Next to the structure of conversations, the discussed content - as
well as their dynamics - have to be taken into account. Still, most existing
modelling and analysis approaches focus only on one of these aspects and, in
particular, lack the capability to investigate the temporal evolution of a
conversation. To address these shortcomings, in this work, we present CODY, a
content-aware, graph-based framework to study the dynamics of online
conversations along multiple dimensions. Its capabilities are extensively
demonstrated by conducting three experiments based on a large conversation
dataset from the German political Twittersphere. First, the posting activity
across the lifetime of conversations is examined. We find that posting activity
follows an exponential saturation pattern. Based on this activity model, we
develop a volume-based sampling method to study conversation dynamics using
temporal network snapshots. In a second experiment, we focus on the evolution
of a conversation's structure and leverage a novel metric, the temporal Wiener
index, for that. Results indicate that as conversations progress, a
conversation's structure tends to be less sprawling and more centered around
the original seed post. Furthermore, focusing on the dynamics of content in
conversations, the evolution of hashtag usage within conversations is studied.
Initially used hashtags do not necessarily keep their dominant prevalence
throughout the lifetime of a conversation. Instead, various "hashtag hijacking"
scenarios are found.
