Jointly modelling the evolution of social structure and language in online communities
This work addresses the need to account for socio-temporal context in online communities, particularly for analyzing extremist groups, though it is incremental as it builds on existing modeling techniques.
The authors tackled the problem of modeling interactions in online communities by jointly modeling community structure and language over time, and found that their approach outperformed prior models lacking these components on clustering and embedding prediction tasks.
Group interactions take place within a particular socio-temporal context, which should be taken into account when modelling interactions in online communities. We propose a method for jointly modelling community structure and language over time. Our system produces dynamic word and user representations that can be used to cluster users, investigate thematic interests of groups, and predict group membership. We apply and evaluate our method in the context of a set of misogynistic extremist groups. Our results indicate that this approach outperforms prior models which lacked one of these components (i.e. not incorporating social structure, or using static word embeddings) when evaluated on clustering and embedding prediction tasks. Our method further enables novel types of analyses on online groups, including tracing their response to temporal events and quantifying their propensity for using violent language, which is of particular importance in the context of extremist groups.