Social media forensics applied to assessment of post–critical incident social reaction: The case of the 2017 Manchester Arena terrorist attack

Bérubé, Maxime, Tang, Thuc-Uyên, Fortin, Francis, Ozlap, Sefa, Williams, Matthew L. ORCID: https://orcid.org/0000-0003-2566-6063 and Burnap, Pete ORCID: https://orcid.org/0000-0003-0396-633X 2020. Social media forensics applied to assessment of post–critical incident social reaction: The case of the 2017 Manchester Arena terrorist attack. Forensic Science International 313 , 110364. 10.1016/j.forsciint.2020.110364

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Abstract

Forensic science is constantly evolving and transforming, reflecting the numerous technological innovations of recent decades. There are, however, continuing issues with the use of digital data, such as the difficulty of handling large-scale collections of text data. As one way of dealing with this problem, we used machine-learning techniques, particularly natural language processing and Latent Dirichlet Allocation (LDA) topic modeling, to create an unsupervised text reduction method that was then used to study social reactions in the aftermath of the 2017 Manchester Arena bombing. Our database was a set of millions of messages posted on Twitter in the first 24 hours after the attack. The findings show that our method improves on the tools presently used by law enforcement and other agencies to monitor social media, particularly following an event that is likely to create widespread social reaction. For example, it makes it possible to track different types of social reactions over time and to identify subevents that have a significant impact on public perceptions.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Social Sciences (Includes Criminology and Education) Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0379-0738
Date of First Compliant Deposit:	16 June 2020
Date of Acceptance:	8 June 2020
Last Modified:	06 Nov 2023 19:30
URI:	https://orca.cardiff.ac.uk/id/eprint/132464

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