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Multimodal Approaches in Analysing and Interpreting Big Social Media Data

Abstract

The general consensus towards the definition of Big data is that it is the data that is too big to manage using conventional methods. Yet, the present Big data approaches will eventually become conventional, where non-specialists can conduct their tasks without the need for consultancy services, much like any standard computing platforms today. In this chapter, we approach the topic from a multimodal perspective but are strategically focused on making meaning out of single-source data using multiple modes, with technologies and data accessible to anyone. We gave attention to social media, Twitter particularly, in order to demonstrate the entire process of our multimodal analysis from acquiring data to the Mixed-Reality approaches in the visualisation of data in near real-time for the future of interpretation. Our argument is that Big data research, which in the past were considered accessible only to corporations with large investment models and academic institutions with large funding streams, should no longer be a barrier. Instead, the bigger issue should be the development of multi-modal approaches to contextualising data so as to facilitate meaningful interpretations.

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Acknowledgments

The author acknowledges the financial support from the International Doctoral Innovation Centre, Ningbo Education Bureau, Ningbo Science and Technology Bureau, and the University of Nottingham. This work was also supported by the UK Engineering and Physical Sciences Research Council [grant number EP/L015463/1]. The equipment provided by NVIDIA is greatly appreciated, without which the freedom of exploratory research and innovation may be constraint to only funded projects.

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Correspondence to Eugene Ch’ng .

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Ch’ng, E., Li, M., Chen, Z., Lang, J., See, S. (2019). Multimodal Approaches in Analysing and Interpreting Big Social Media Data. In: Seng, K., Ang, Lm., Liew, AC., Gao, J. (eds) Multimodal Analytics for Next-Generation Big Data Technologies and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-97598-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-97598-6_15

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