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Title: Grandmaster: Interactive text-based analytics of social media [PowerPoint]

Abstract

People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories.,
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
1331848
Report Number(s):
SAND2015-9732C
607702
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the Workshop on Social Multimedia Data Mining at IEEE International Conference on Data Mining held November 14-17, 2015 in Atlantic City, NJ.
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Fabian, Nathan D., Davis, Warren Leon,, Raybourn, Elaine M., Lakkaraju, Kiran, and Whetzel, Jonathan. Grandmaster: Interactive text-based analytics of social media [PowerPoint]. United States: N. p., 2015. Web. doi:10.1109/ICDMW.2015.187.
Fabian, Nathan D., Davis, Warren Leon,, Raybourn, Elaine M., Lakkaraju, Kiran, & Whetzel, Jonathan. Grandmaster: Interactive text-based analytics of social media [PowerPoint]. United States. doi:10.1109/ICDMW.2015.187.
Fabian, Nathan D., Davis, Warren Leon,, Raybourn, Elaine M., Lakkaraju, Kiran, and Whetzel, Jonathan. Sun . "Grandmaster: Interactive text-based analytics of social media [PowerPoint]". United States. doi:10.1109/ICDMW.2015.187. https://www.osti.gov/servlets/purl/1331848.
@article{osti_1331848,
title = {Grandmaster: Interactive text-based analytics of social media [PowerPoint]},
author = {Fabian, Nathan D. and Davis, Warren Leon, and Raybourn, Elaine M. and Lakkaraju, Kiran and Whetzel, Jonathan},
abstractNote = {People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.},
doi = {10.1109/ICDMW.2015.187},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {11}
}

Conference:
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