Cited By
View all- Papapesios NEllul CShakir AHart G(2018)Exploring the use of crowdsourced geographic information in defence: challenges and opportunitiesJournal of Geographical Systems10.1007/s10109-018-0282-5Online publication date: 17-Dec-2018
Crowdsourcing Social Media for Military Operations
Authors: 
H. Roy, S. E. Kase, E. K. BowmanAuthors Info & Claims
Pages 23 - 27
Abstract
In this paper, we consider the demographics associated with social media users as a basis for determining how to interact with a population group to inform military operations such as humanitarian aid and disaster relief (HADR). With social media use increasing across most societal groups, information can be shared in a more representative manner than a decade ago. Also, crowdsourcing activities can be more productive and useful as the percentage of citizens using this technology increases. We discuss a recent experiment using the Amazon Mechanical Turk platform to investigate social bias factors associated with information transmission. 759 participants shared their social media usage characteristics as a feature of that study, and we explore those data in this paper to consider social media uses for HADR scenarios. We provide demographic characteristics for ten major social media platforms and discuss how tailored crowdsourcing would benefit decision making in traumatic and confusing conditions.
References
[1]
Perrin, A. 2015. Social Networking Usage: 2005-2015. Pew Research Center Report. Available at: http://www.pewinternet.org/2015/10/08/2015/Social-Networking-Usage-2005-2015/.
[2]
Joint Publication 2-01.3. 2014. Joint Intelligence Preparation of the Operational Environment. Washington, D.C. Available at: http://www.defenseinnovationmarketplace.mil/resources/12102012_io1.pdf
[3]
Roy, H. E., Bowman, E. K., Kase, S. E., and Abdelzaher, T. 2016. Investigating social bias in information transmission: Experimental design and preliminary analyses. In Proceedings of the 21st International Command & Control Research & Technology Symposium (ICCRTS), London, UK.
[4]
Statista. (2015). Leading social media websites in the United States in August 2015, based on share of visits. Retrieved November, 2015 from http://www.statista.com/statistics/265773/market-share-of-the-most-popular-social-media-websites-in-the-us/
[5]
Hauser, D. J., & Schwarz, N. (2016). Attentive turkers: Mturk participants perform better on online attention checks than do subject pool participants. Behavior Research Methods, 48(1), 400--407.
[6]
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3--5.
[7]
Peer, E., Vosgerau, J., & Acquisti, A. (2013). Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behavior Research Methods, 1--9.
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Published In
April 2017
97 pages
ISBN:9781450349772
DOI:10.1145/3055601
Copyright © 2017 ACM.
© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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- IEEE Signal Processing Society
- SIGBED: ACM Special Interest Group on Embedded Systems
- IEEE CS
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Association for Computing Machinery
New York, NY, United States
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Published: 18 April 2017
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Cited By
View all- Papapesios NEllul CShakir AHart G(2018)Exploring the use of crowdsourced geographic information in defence: challenges and opportunitiesJournal of Geographical Systems10.1007/s10109-018-0282-5Online publication date: 17-Dec-2018
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