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, Daniel
N1 - Funding Information:
This paper is based upon work supported partially by funding from the U.S. Department of Homeland Security (through the 2013 Summer Research Team Program for MSI) and U.S. National Science Foundation (DUE-1141209) and by the National Center for Border Security and Immigration at the University of Arizona, and by the Center for Business Intelligence and Analytics at Stetson University (http://cbia.stetson.edu/). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of funding agencies. We thank Mr. Daniel Ballard, the editor and reviewers, and coordinators for their assistance and valuable suggestions.
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 -