Harris, Martyn and Jacobson, Jessica and Provetti, Alessandro (2024) Sentiment and time-series analysis of direct-message conversations. Forensic Science International: Digital Investigation 49 (301753), pp. 1-14. ISSN 2666-2825.
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Abstract
Social media and mobile communications in general are an extremely rich source of digital forensic information. We present our new framework for analysing this resource with an innovative combination of time series and text mining methods. The framework is intended to create a tool to analyse and operationally summarise extended trails of social media messages, thus enabling investigators for the first time to drill down into specific moments at which sentiment analysis has detected a change of tone indicative of a particularly strong and significant response. Crucially, the method will give investigators an opportunity to reduce the time and resource commitment required for ongoing and hands-on analysis of digital communications on media such as Texts/SMS, WhatsApp and Messenger.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | Text analysis, Sentiment analysis, Access to mobile data, Digital forensics |
| School: | Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Law School Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Crime & Justice Policy Research, Institute for |
| Depositing User: | Alessandro Provetti |
| Date Deposited: | 12 Jun 2024 12:32 |
| Last Modified: | 12 Jun 2024 15:34 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/53681 |
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