Social polling
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Social polling is a form of open access polling, which combines social media and opinion polling. In contrast to tradition polling the polls are formulated by the respondents themselves.[1][2][3][4]
Social polling is an example of nonprobability sampling that uses self-selection rather than a statistical sampling scheme.[5] Social polling also allows quick feedback since responses are obtained via social media platforms such as Facebook, Twitter, and blogs.[6] A sentiment analytics tool can be employed to monitor the poll or the topics of discussion.[7] This method can evaluate information obtained via social media posts through two paradigms: "top down" and "bottom up".[7]
See also
[edit]References
[edit]- ^ Waxman, Olivia B. (2012). "Polling and Social Media Collide with 'Social Polling'". Time. ISSN 0040-781X. Retrieved 8 June 2016.
- ^ Eha, Brian Patrick (9 August 2013). "Hot or Not: Social Polling Startups Take the Temperature of the Masses". www.entrepreneur.com. entrepreneur. Retrieved 20 June 2016.
- ^ Gaspers, Serge; Naroditskiy, Victor; Narodytska, Nina; Walsh, Toby (1 January 2014). "Possible and Necessary Winner Problem in Social Polls". Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems. International Foundation for Autonomous Agents and Multiagent Systems: 613–620. arXiv:1302.1669. Bibcode:2013arXiv1302.1669G.
- ^ Yasseri, Taha; Bright, Jonathan (28 January 2014). "Can electoral popularity be predicted using socially generated big data?". It - Information Technology. 56 (5): 246–253. arXiv:1312.2818. doi:10.1515/itit-2014-1046. S2CID 12014052.
- ^ Kennedy, Courtney; Caumont, Andrea (2 May 2016). "What we learned about online nonprobability polls". www.pewresearch.org. Pew Research Center. Retrieved 20 June 2016.
- ^ Mozer, Mindy (2014). Social Network-Powered Education Opportunities. New York: The Rosen Publishing Group, Inc. p. 31. ISBN 9781477716823.
- ^ a b Mazumder, Sourav; Bhadoria, Robin Singh; Deka, Ganesh Chandra (2017). Distributed Computing in Big Data Analytics: Concepts, Technologies and Applications. Cham, Switzerland: Springer. pp. 127, 130. ISBN 9783319598338.
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