A Social Sensing Model for Event Detection and User Influence Discovering in Social Media Data Streams
- Submitting institution
-
The University of Leicester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2172
- Type
- D - Journal article
- DOI
-
10.1109/TCSS.2019.2938954
- Title of journal
- IEEE Transactions on Computational Social Systems
- Article number
- 1
- First page
- 141
- Volume
- 7
- Issue
- 1
- ISSN
- 2329-924X
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
https://doi.org/10.1109/TCSS.2019.2938954
- 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
-
5
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Global views, of distributed systems composed from multiple local systems, often provide excellent models for properties of the complete system. Providers of social networks want to capture users' influence statistics across a global network as the network dynamically evolves, and not be restricted to obtaining user's local influence statistics. This gives the provider a more reliable data model. Our Social Sensing Model DPRank provides a solution. This has the potential to give providers tools that better extrapolate users' global influence. Such potential is demonstrated by an application to real-world Twitter datasets with comparisons to existing methods.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -