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Using semantic clustering to support situation awareness on Twitter: The case of World Views

Kingston, Charlie, Nurse, Jason R. C., Agrafiotis, Ioannis, Milich, Andrew (2018) Using semantic clustering to support situation awareness on Twitter: The case of World Views. Human-centric Computing and Information Sciences, . ISSN 2192-1962. E-ISSN 2192-1962. (doi:10.1186/s13673-018-0145-6) (KAR id:67656)

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http://dx.doi.org/10.1186/s13673-018-0145-6

Abstract

In recent years, situation awareness has been recognised as a critical part of effective decision making, in particular for crisis management. One way to extract value and allow for better situation awareness is to develop a system capable of analysing a dataset of multiple posts, and clustering consistent posts into different views or stories (or, world views). However, this can be challenging as it requires an understanding of the data, including determining what is consistent data, and what data corroborates other data. Attempting to address these problems, this article proposes Subject-Verb-Object Semantic Suffix Tree Clustering (SVOSSTC) and a system to support it, with a special focus on Twitter content. The novelty and value of SVOSSTC is its emphasis on utilising the Subject-Verb-Object (SVO) typology in order to construct semantically consistent world views, in which individuals---particularly those involved in crisis response---might achieve an enhanced picture of a situation from social media data. To evaluate our system and its ability to provide enhanced situation awareness, we tested it against existing approaches, including human data analysis, using a variety of real-world scenarios. The results indicated a noteworthy degree of evidence (e.g., in cluster granularity and meaningfulness) to affirm the suitability and rigour of our approach. Moreover, these results highlight this article's proposals as innovative and practical system contributions to the research field.

Item Type: Article
DOI/Identification number: 10.1186/s13673-018-0145-6
Uncontrolled keywords: social media analytics; user-support tools; data clustering; information systems; crisis response; computational social science; fake news
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics (inc Computing science)
T Technology
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Security Group
Faculties > Sciences > School of Computing > Data Science
Faculties > Social Sciences > School of Social Policy Sociology and Social Research
Depositing User: Jason Nurse
Date Deposited: 17 Jul 2018 17:14 UTC
Last Modified: 01 Aug 2019 10:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67656 (The current URI for this page, for reference purposes)
Nurse, Jason R. C.: https://orcid.org/0000-0003-4118-1680
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