Better Intelligence Through Microblogging: Text Mining Tweets for Actionable Information

Public Deposited
Analytics

Downloadable Content

Download PDF
Request Version for Screen Reader
Last Modified
  • February 28, 2019
Creator
  • Croll, Meghan
    • Affiliation: School of Information and Library Science
Abstract
  • The goal of this paper is to evaluate specific event-related Twitter posts, called tweets, for their usability as intelligence data. Using text mining software for Natural Language Processing to attempt to establish the best keyword search strategy for actionable information, 2000 tweets documenting 2 independent events were analyzed. Additionally, the Twitter users who provided the tweets were evaluated for source reliability to determine if their information was likely to be credible. The results of this study suggest that the best information retrieval strategy for tweets featuring reliable and actionable information, is to use hashtagged topics and/or location details as search keywords. Not only is "location" frequently a facet of determining actionability, the co-location of event and user increases the likelihood of a high-reliability source.
Date of publication
Subject
DOI
Resource type
Rights statement
  • In Copyright
Advisor
  • Haas, Stephanie W.
Degree
  • Master of Science in Library Science
Degree granting institution
  • University of North Carolina at Chapel Hill
Extent
  • 52
Deposit record
  • 510abea9-adc0-4aed-bcd0-691a167314e6

Relations

Parents:

This work has no parents.

Items

Thumbnail Title Date Uploaded Visibility Actions
file details Better Intelligence Through Microblogging: Text Mining Tweets for Actionable Information 2019-05-13 Public Download