Geoparsing and geosemantics for social media : spatio-temporal grounding of content propagating rumours to support trust and veracity analysis during breaking news
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 41296276
- Type
- D - Journal article
- DOI
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10.1145/2842604
- Title of journal
- ACM Transactions on Information Systems
- Article number
- 16
- First page
- 1
- Volume
- 34
- Issue
- 3
- ISSN
- 1046-8188
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2016
- URL
-
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- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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1
- Research group(s)
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-
- Citation count
- 16
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Paper describes a geosemantic classification algorithm for identifying eyewitness twitter content to debunk fake news. Work attracted interest from UK law enforcement (UK Border Force, National Crime Agency) and UK MOD with follow-on grants building on this work (ESRC ES/R003254/1, £300,456 UoS) (DSTL ACC102157, £83,196 UoS) (DSTL ACC2005442, £116,405 UoS). Work led to Middleton being invited as an expert to attend ATI/DSTL workshop-2019 and UK Ministerial AI Roundtable 2019 (chaired by Policing Minister Nick Hurd). Presented work at SciCar-2018 (invited talk & panel) and WWW-2015 workshop RDSM-2015 (invited talk).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
