An unsupervised framework of exploring events on Twitter : filtering, extraction and categorization
- Submitting institution
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The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6003
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI)
- First page
- 2468
- Volume
- -
- Issue
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- ISSN
- -
- Open access status
- -
- Month of publication
- February
- Year of publication
- 2015
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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I - Artificial Intelligence and Human-Centred Computing
- Citation count
- 23
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in one of the two top AI conferences, this paper proposed the first unsupervised framework for event extraction and categorisation on Twitter. Cited by leading experts in NLP and machine learning such as Tat-Seng Chua (National University of Singapore) and Chengxiang, Zhai (UIUC), it impacted work of influence modelling for collective search behaviour (Santu, UIUC; Li, Yahoo), wellness event detection from Twitter (Akbari, UCL; Hu, Texas A&M), and classification of personal health-experience tweets (Calix, Purdue). This research formed the foundation of the recently funded UKRI COVID-19 project on an AI-enabled evidence-driven framework for claim veracity assessment during pandemics.
- Author contribution statement
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- Non-English
- No
- English abstract
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