Details of Research Outputs

TitleMultimodal approaches in analysing and interpreting big social media data
Creator
Date Issued2019
Source PublicationMultimodal analytics for next-generation big data technologies and applications
ISBN9783319975979
Author/Editor of Source PublicationKah Phooi Seng, Li-minn Ang, Alan Wee-Chung Liew, Junbin Gao
Publication PlaceSwitzerland
PublisherSpringer, Cham
Pages361-391
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.

Language英语English
URLView source
Document TypeBook chapter
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/11044
CollectionResearch outside affiliated institution
Affiliation
University of Nottingham Ningbo China, Ningbo, China
Recommended Citation
GB/T 7714
Ch'ng, Eugene,Li, Mengdi,Chen, Ziyanget al. Multimodal approaches in analysing and interpreting big social media data. Switzerland: Springer, Cham, 2019: 361-391.
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