A multimodal analysis of humour as an engagement strategy in YouTube research dissemination videos


science dissemination

How to Cite

Bernad-Mechó, E., & Girón-García, C. (2023). A multimodal analysis of humour as an engagement strategy in YouTube research dissemination videos. The European Journal of Humour Research, 11(1), 46–66. https://doi.org/10.7592/EJHR.2023.11.1.760


Science popularisation has received widespread interest in the last decade. With the rapid evolution from print to digital modes of information, science outreach has been seen to cross educational boundaries and become integrated into wider contexts such as YouTube.  One of the main features of the success of research dissemination videos on YouTube is the ability to establish a meaningful connection with the audience. In this regard, humour may be used as a strategy for engagement. Most studies on humour, however, are conducted solely from a purely linguistic perspective, obviating the complex multimodal reality of communication in the digital era. Considering this background, we set out to explore how humour is used from a multimodal point of view as an engagement strategy in YouTube research dissemination. We selected three research dissemination videos from three distinct YouTube channels to fulfil this aim. After an initial viewing, 22 short humoristic fragments that were particularly engaging were selected. These fragments were further explored using Multimodal Analysis - Video (MAV)[1], a multi-layered annotation tool that allows for fine-grained multimodal analysis. Humoristic strategies and contextual features were explored, as well as two main types of modes: embodied and filmic. Results show the presence of 9 linguistic strategies to introduce humour in YouTube science dissemination videos which are always accompanied by heterogeneous combinations of embodied and filmic modes that contribute to fully achieving humoristic purposes.

[1] Multi-layer annotation software used to describe the use of semiotic modes in video files. By using this software, researchers may analyse, for instance, how gestures, gaze, proxemics, head movements, facial expression, etc. are employed in a given file.



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