  The explosion in the amount of news and journalistic content being generated
across the globe, coupled with extended and instantaneous access to information
through online media, makes it difficult and time-consuming to monitor news
developments and opinion formation in real time. There is an increasing need
for tools that can pre-process, analyse and classify raw text to extract
interpretable content; specifically, identifying topics and content-driven
groupings of articles. We present here such a methodology that brings together
powerful vector embeddings from Natural Language Processing with tools from
Graph Theory that exploit diffusive dynamics on graphs to reveal natural
partitions across scales. Our framework uses a recent deep neural network text
analysis methodology (Doc2vec) to represent text in vector form and then
applies a multi-scale community detection method (Markov Stability) to
partition a similarity graph of document vectors. The method allows us to
obtain clusters of documents with similar content, at different levels of
resolution, in an unsupervised manner. We showcase our approach with the
analysis of a corpus of 9,000 news articles published by Vox Media over one
year. Our results show consistent groupings of documents according to content
without a priori assumptions about the number or type of clusters to be found.
The multilevel clustering reveals a quasi-hierarchy of topics and subtopics
with increased intelligibility and improved topic coherence as compared to
external taxonomy services and standard topic detection methods.
