  Chatbots, taking advantage of the success of the messaging apps and recent
advances in Artificial Intelligence, have become very popular, from helping
business to improve customer services to chatting to users for the sake of
conversation and engagement (celebrity or personal bots). However, developing
and improving a chatbot requires understanding their data generated by its
users. Dialog data has a different nature of a simple question and answering
interaction, in which context and temporal properties (turn order) creates a
different understanding of such data. In this paper, we propose a novelty
metric to compute dialogs' similarity based not only on the text content but
also on the information related to the dialog structure. Our experimental
results performed over the Switchboard dataset show that using evidence from
both textual content and the dialog structure leads to more accurate results
than using each measure in isolation.
