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Title: Temporal Methods to Detect Content-Based Anomalies in Social Media

Abstract

Here, we develop a method for time-dependent topic tracking and meme trending in social media. Our objective is to identify time periods whose content differs signifcantly from normal, and we utilize two techniques to do so. The first is an information-theoretic analysis of the distributions of terms emitted during different periods of time. In the second, we cluster documents from each time period and analyze the tightness of each clustering. We also discuss a method of combining the scores created by each technique, and we provide ample empirical analysis of our methodology on various Twitter datasets.

Authors:
 [1];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1429685
Report Number(s):
SAND-2017-12340J
658756
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Program Document
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS; 97 MATHEMATICS AND COMPUTING

Citation Formats

Skryzalin, Jacek, Field, Jr., Richard, Fisher, Andrew N., and Bauer, Travis L. Temporal Methods to Detect Content-Based Anomalies in Social Media. United States: N. p., 2017. Web.
Skryzalin, Jacek, Field, Jr., Richard, Fisher, Andrew N., & Bauer, Travis L. Temporal Methods to Detect Content-Based Anomalies in Social Media. United States.
Skryzalin, Jacek, Field, Jr., Richard, Fisher, Andrew N., and Bauer, Travis L. Wed . "Temporal Methods to Detect Content-Based Anomalies in Social Media". United States.
@article{osti_1429685,
title = {Temporal Methods to Detect Content-Based Anomalies in Social Media},
author = {Skryzalin, Jacek and Field, Jr., Richard and Fisher, Andrew N. and Bauer, Travis L.},
abstractNote = {Here, we develop a method for time-dependent topic tracking and meme trending in social media. Our objective is to identify time periods whose content differs signifcantly from normal, and we utilize two techniques to do so. The first is an information-theoretic analysis of the distributions of terms emitted during different periods of time. In the second, we cluster documents from each time period and analyze the tightness of each clustering. We also discuss a method of combining the scores created by each technique, and we provide ample empirical analysis of our methodology on various Twitter datasets.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {11}
}

Program Document:
Other availability
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