Journal article
Perceptions of presidential candidates' personalities in twitter
Journal of the Association for Information Science and Technology, Vol.67(2), pp.249-267
02/2016
DOI: 10.1002/asi.23377
Abstract
Political sentiment analysis using social media, especially Twitter, has attracted wide interest in recent years. In such research, opinions about politicians are typically divided into positive, negative, or neutral. In our research, the goal is to mine political opinion from social media at a higher resolution by assessing statements of opinion related to the personality traits of politicians; this is an angle that has not yet been considered in social media research. A second goal is to contribute a novel retrieval‐based approach for tracking public perception of personality using Gough and Heilbrun's Adjective Check List (ACL) of 110 terms describing key traits. This is in contrast to the typical lexical and machine‐learning approaches used in sentiment analysis. High‐precision search templates developed from the ACL were run on an 18‐month span of Twitter posts mentioning Obama and Romney and these retrieved more than half a million tweets. For example, the results indicated that Romney was perceived as more of an achiever and Obama was perceived as somewhat more friendly. The traits were also aggregated into 14 broad personality dimensions. For example, Obama rated far higher than Romney on the Moderation dimension and lower on the Machiavellianism dimension. The temporal variability of such perceptions was explored.
Details
- Title: Subtitle
- Perceptions of presidential candidates' personalities in twitter
- Creators
- Sanmitra Bhattacharya - The University of IowaChao Yang - The University of IowaPadmini Srinivasan - The University of IowaBob Boynton - The University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of the Association for Information Science and Technology, Vol.67(2), pp.249-267
- DOI
- 10.1002/asi.23377
- ISSN
- 2330-1635
- eISSN
- 2330-1643
- Number of pages
- 19
- Language
- English
- Date published
- 02/2016
- Academic Unit
- Nursing; Computer Science; Business Analytics; Political Science
- Record Identifier
- 9984003181802771
Metrics
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