Automatic identification of abstract online groups
Abstract
Online abstract groups, in which members aren't explicitly connected, can be automatically identified by computer-implemented methods. The methods involve harvesting records from social media and extracting content-based and structure-based features from each record. Each record includes a social-media posting and is associated with one or more entities. Each feature is stored on a data storage device and includes a computer-readable representation of an attribute of one or more records. The methods further involve grouping records into record groups according to the features of each record. Further still the methods involve calculating an n-dimensional surface representing each record group and defining an outlier as a record having feature-based distances measured from every n-dimensional surface that exceed a threshold value. Each of the n-dimensional surfaces is described by a footprint that characterizes the respective record group as an online abstract group.
- Inventors:
- Issue Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1129185
- Patent Number(s):
- 8,700,629
- Application Number:
- 13/540,759
- Assignee:
- Battelle Memorial Institute (Richland, WA)
- DOE Contract Number:
- AC05-76RLO1830
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2012 Jul 03
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Engel, David W, Gregory, Michelle L, Bell, Eric B, Cowell, Andrew J, and Piatt, Andrew W. Automatic identification of abstract online groups. United States: N. p., 2014.
Web.
Engel, David W, Gregory, Michelle L, Bell, Eric B, Cowell, Andrew J, & Piatt, Andrew W. Automatic identification of abstract online groups. United States.
Engel, David W, Gregory, Michelle L, Bell, Eric B, Cowell, Andrew J, and Piatt, Andrew W. Tue .
"Automatic identification of abstract online groups". United States. https://www.osti.gov/servlets/purl/1129185.
@article{osti_1129185,
title = {Automatic identification of abstract online groups},
author = {Engel, David W and Gregory, Michelle L and Bell, Eric B and Cowell, Andrew J and Piatt, Andrew W},
abstractNote = {Online abstract groups, in which members aren't explicitly connected, can be automatically identified by computer-implemented methods. The methods involve harvesting records from social media and extracting content-based and structure-based features from each record. Each record includes a social-media posting and is associated with one or more entities. Each feature is stored on a data storage device and includes a computer-readable representation of an attribute of one or more records. The methods further involve grouping records into record groups according to the features of each record. Further still the methods involve calculating an n-dimensional surface representing each record group and defining an outlier as a record having feature-based distances measured from every n-dimensional surface that exceed a threshold value. Each of the n-dimensional surfaces is described by a footprint that characterizes the respective record group as an online abstract group.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2014},
month = {4}
}
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