Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter

Zhou, Xujuan and Coiera, Enrico and Tsafnat, Guy and Arachi, Diana and Ong, Mei-Sing and Dunn, Adam G. (2015) Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter. In: 15th World Congress on Health and Biomedical Informatics MEDINFO 2015, 19-23 Aug 2015, São Paulo, Brazil.

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Abstract

The manner in which people preferentially interact with others like themselves suggests that information about social connections may be useful in the surveillance of opinions for public health purposes. We examined if social connection information from tweets about human papillomavirus (HPV) vaccines could be used to train classifiers that identify antivaccine opinions. From 42,533 tweets posted between October 2013 and March 2014, 2,098 were sampled at random and two investigators independently identified anti-vaccine opinions. Machine learning methods were used to train classifiers using the first three months of data, including content (8,261 text fragments) and social connections (10,758 relationships). Connection-based classifiers performed similarly to content-based classifiers on the first three months of training data, and performed more consistently than content-based classifiers on test data from the subsequent three months. The most accurate classifier achieved an accuracy of 88.6% on the test data set, and used only social connection features. Information about how people are connected, rather than what they write, may be useful for improving public health surveillance methods on Twitter.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 12 Sep 2016 05:33
Last Modified: 12 Sep 2016 05:33
Uncontrolled Keywords: Machine learning; Social media; HPV vaccines; Public health surveillance; Twitter messaging; Text mining
Fields of Research : 11 Medical and Health Sciences > 1117 Public Health and Health Services > 111799 Public Health and Health Services not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
08 Information and Computing Sciences > 0807 Library and Information Studies > 080799 Library and Information Studies not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
Identification Number or DOI: 10.3233/978-1-61499-564-7-761
URI: http://eprints.usq.edu.au/id/eprint/29686

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