Putting Context in Context: the Impact of Discussion Structure on Text
  Classification
Nicolò Penzo Antonio Longa Bruno Lepri Sara Tonelli Marco Guerini
Current text classification approaches usually focus on the content to be
classified. Contextual aspects (both linguistic and extra-linguistic) are
usually neglected, even in tasks based on online discussions. Still in many
cases the multi-party and multi-turn nature of the context from which these
elements are selected can be fruitfully exploited. In this work, we propose a
series of experiments on a large dataset for stance detection in English, in
which we evaluate the contribution of different types of contextual
information, i.e. linguistic, structural and temporal, by feeding them as
natural language input into a transformer-based model. We also experiment with
different amounts of training data and analyse the topology of local discussion
networks in a privacy-compliant way. Results show that structural information
can be highly beneficial to text classification but only under certain
circumstances (e.g. depending on the amount of training data and on discussion
chain complexity). Indeed, we show that contextual information on smaller
datasets from other classification tasks does not yield significant
improvements. Our framework, based on local discussion networks, allows the
integration of structural information, while minimising user profiling, thus
preserving their privacy.