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Topic modelling is a branch of Natural Language Processing (NLP) that deals with the discovery of conversation topics in a given document corpus. In social media, this translates into aggregating social media posts, e.g. tweets, into topics of conversation and observing how these topics evolve over time (hence the “dynamic” adjective). Conveying the results of topic modelling can be challenging since the topics often do not lend themselves naturally to meaningful labelling. The volume of real world (global) social media also means that millions of topics can be ongoing at any given time and the relationships between them can involve hundreds of dimensions and relationships that continually emerge. The popularity of topics is itself subject to change over time and reflect the pulse of what is happening in society at large. In this paper, we propose a spatialization technique based on open-source software that reduces the intrinsic complexity of dynamic topic modelling results to familiar topographic objects, namely: ridges, valleys, and peaks. This offers new possibilities for understanding complex relationships that change over time whilst overcoming issues with traditional topic modelling visualisation approaches such as network graphs.

Pollinator communities could have nuanced effects on crop yield depending on their species and functional trait compositions. Here, the authors use a meta-analysis to show that, in addition to pollinator abundance, functional trait divergence also positively impacts yield of oilseed rape crops.