A Rule Dynamics Approach to Event Detection in Twitter with Its Application to Sports and Politics
- Submitting institution
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Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D0005
- Type
- D - Journal article
- DOI
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10.1016/j.eswa.2016.02.028
- Title of journal
- Expert Systems with Applications
- Article number
- -
- First page
- 351
- Volume
- 55
- Issue
- -
- ISSN
- 0957-4174
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2016
- URL
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https://www.sciencedirect.com/science/article/pii/S0957417416300598
- 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
-
-
- Research group(s)
-
-
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Transaction-based Rule Change Mining (TRCM) is a novel method for the automation of event detection and tracking on Twitter in one cohesive computational framework. The application of TRCM method for the automation of event detection on Twitter on datasets of different dynamic nature proves its universality in a number of application domains. TRCM has inspired more work in event detection and tracking on Twitter. The work of Devika et al. (https://hal.archives-ouvertes.fr/hal-01826698) adopted TRCM as benchmark for their method ECLAT using Apriori algorithm. Additionally, the authors (https://doi.org/10.1016/j.procs.2018.10.415) adopted TRCM Methods as benchmark using Twitter word co-occurrence model for event detection.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -