Sentinel: a co-designed platform for semantic enrichment of social media streams

Preece, Alun ORCID: https://orcid.org/0000-0003-0349-9057, Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885, Evans, Kieran ORCID: https://orcid.org/0000-0003-0414-0812, Rogers, David, Webberley, William, Roberts, Colin ORCID: https://orcid.org/0000-0003-0595-2740 and Innes, Martin ORCID: https://orcid.org/0000-0002-8950-8147 2018. Sentinel: a co-designed platform for semantic enrichment of social media streams. IEEE Transactions on Computational Social Systems 5 (1) , pp. 118-131. 10.1109/TCSS.2017.2763684

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Abstract

We introduce the Sentinel platform that supports semantic enrichment of streamed social media data for the purposes of situational understanding. The platform is the result of a codesign effort between computing and social scientists, iteratively developed through a series of pilot studies. The platform is founded upon a knowledge-based approach, in which input streams (channels) are characterized by spatial and terminological parameters, collected media is preprocessed to identify significant terms (signals), and data are tagged (framed) in relation to an ontology. Interpretation of processed media is framed in terms of the 5W framework (who, what, when, where, and why). The platform is designed to be open to the incorporation of new processing modules, building on the knowledge-based elements (channels, signals, and framing ontology) and accessible via a set of user-facing apps. We present the conceptual architecture for the platform, discuss the design and implementation challenges of the underlying streamprocessing system, and present a number of apps developed in the context of the pilot studies, highlighting the strengths and importance of the codesign approach and indicating promising areas for future research.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Social Sciences (Includes Criminology and Education) Crime and Security Research Institute (CSURI)
Additional Information:	This work is licensed under a Creative Commons Attribution 3.0 License.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	2329-924X
Date of First Compliant Deposit:	13 October 2017
Date of Acceptance:	28 September 2017
Last Modified:	17 May 2023 11:59
URI:	https://orca.cardiff.ac.uk/id/eprint/105513

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