Abstract
Social media provide opportunities for policy makers to gauge pubic opinion. However, the large volumes and variety of expressions on social media have challenged traditional policy analysis and public sentiment assessment. In this article, we describe a framework for social-media-based public policy informatics and a system called “iMood” that addresses the needs for sentiment and network analyses of U.S. immigration and border security. iMood collects related messages on Twitter, extracts user sentiment and emotion, and constructs networks of the Twitter users, helping policy makers to identify opinion leaders, influential users, and community activists. We evaluated the sentiment, emotion, and network characteristics found in 909,035 tweets posted by over 300,000 users during three phases between May and November 2013. Statistical analyses reveal significant differences in emotion and sentiment among the 3 phases. The Twitter networks of the 3 phases also had significantly different relationship counts, network densities, and total influence scores from those of other phases. This research should contribute to developing a new framework and a new system for social-media-based public policy informatics, providing new empirical findings and data sets of sentiment and network analyses of U.S. immigration and border security, and demonstrating a general applicability to different domains.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1588-1606 |
| Number of pages | 19 |
| Journal | Journal of the Association for Information Science and Technology |
| Volume | 67 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 1 2016 |
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Keywords
- knowledge organization systems
- network analysis
- public domain information
ASJC Scopus subject areas
- Information Systems
- Computer Networks and Communications
- Information Systems and Management
- Library and Information Sciences
Cite this
Social-media-based public policy informatics : Sentiment and network analyses of U.S. Immigration and border security. / Chung, Wingyan; Zeng, Dajun.
In: Journal of the Association for Information Science and Technology, Vol. 67, No. 7, 01.07.2016, p. 1588-1606.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Social-media-based public policy informatics
T2 - Sentiment and network analyses of U.S. Immigration and border security
AU - Chung, Wingyan
AU - Zeng, Dajun
PY - 2016/7/1
Y1 - 2016/7/1
N2 - Social media provide opportunities for policy makers to gauge pubic opinion. However, the large volumes and variety of expressions on social media have challenged traditional policy analysis and public sentiment assessment. In this article, we describe a framework for social-media-based public policy informatics and a system called “iMood” that addresses the needs for sentiment and network analyses of U.S. immigration and border security. iMood collects related messages on Twitter, extracts user sentiment and emotion, and constructs networks of the Twitter users, helping policy makers to identify opinion leaders, influential users, and community activists. We evaluated the sentiment, emotion, and network characteristics found in 909,035 tweets posted by over 300,000 users during three phases between May and November 2013. Statistical analyses reveal significant differences in emotion and sentiment among the 3 phases. The Twitter networks of the 3 phases also had significantly different relationship counts, network densities, and total influence scores from those of other phases. This research should contribute to developing a new framework and a new system for social-media-based public policy informatics, providing new empirical findings and data sets of sentiment and network analyses of U.S. immigration and border security, and demonstrating a general applicability to different domains.
AB - Social media provide opportunities for policy makers to gauge pubic opinion. However, the large volumes and variety of expressions on social media have challenged traditional policy analysis and public sentiment assessment. In this article, we describe a framework for social-media-based public policy informatics and a system called “iMood” that addresses the needs for sentiment and network analyses of U.S. immigration and border security. iMood collects related messages on Twitter, extracts user sentiment and emotion, and constructs networks of the Twitter users, helping policy makers to identify opinion leaders, influential users, and community activists. We evaluated the sentiment, emotion, and network characteristics found in 909,035 tweets posted by over 300,000 users during three phases between May and November 2013. Statistical analyses reveal significant differences in emotion and sentiment among the 3 phases. The Twitter networks of the 3 phases also had significantly different relationship counts, network densities, and total influence scores from those of other phases. This research should contribute to developing a new framework and a new system for social-media-based public policy informatics, providing new empirical findings and data sets of sentiment and network analyses of U.S. immigration and border security, and demonstrating a general applicability to different domains.
KW - knowledge organization systems
KW - network analysis
KW - public domain information
UR - http://www.scopus.com/inward/record.url?scp=84973894727&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973894727&partnerID=8YFLogxK
U2 - 10.1002/asi.23449
DO - 10.1002/asi.23449
M3 - Article
AN - SCOPUS:84973894727
VL - 67
SP - 1588
EP - 1606
JO - Journal of the Association for Information Science and Technology
JF - Journal of the Association for Information Science and Technology
SN - 2330-1635
IS - 7
ER -