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Up to date, linguistic rhythm has been studied for speech, but the rhythm of written texts has been merely recognized, and not analyzed or interpreted in connection to natural language tasks. We provide an extension of the textual rhythmic features we proposed in previous work, and demonstrate its benefits for the task of text categorization. Rhythmic features require that the text be segmented in rhythmic units, the sentence being the default unit. We compute our features using three kinds of rhythmic units, provide a comparison between their relevance, and use the results to outline the rhetorical nature of rhythm. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry. In this paper we investigate the relevance of rhythmic features for categorizing texts in prosaic form pertaining to different genres. Our contribution is threefold. First, we define a set of rhythmic features for written texts. Second, we extract these features from three corpora, of speeches, essays, and newspaper articles. Third, we perform feature selection by means of statistical analyses, and determine a subset of features which efficiently discriminates between the three genres. We find that using as little as eight rhythmic features, documents can be adequately assigned to a given genre with an accuracy of around 80 %, significantly higher than the 33 % baseline which results from random assignment. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

With half of the world already connected to the Internet, we are facing a growing amount of information available online, that is expected to increase exponentially in the following years. Educational environments are transitioning from closed structures to open, collaborative environments, using technology to build virtual classrooms. In this paper we present a customized crawler dedicated to alternative knowledge building environments used for potential community inquiry, that is unique in its power to combine data extraction and indexing capabilities that facilitate discourse-driven community network analysis integrated into the ReaderBench framework. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.