Chung, W., & Zeng, D. (2016). Social-media-based public policy informatics: Sentiment and network analyses of U.S. Immigration and border security. Journal of the Association for Information Science and Technology, 67(7), 1588—1606.
This article presents a case study from the domain of policy informatics - i.e. the field of finding effective ways to use information technology to understand and tackle complex problems of society - which describes how Web intelligence has been successfully applied to assess the public sentiment towards the U.S. immigration and border security.
- a framework for collecting relevant Tweets from Twitter
- sentiment analysis using eight emotional categories defined in Plutchik (1980): anger, disgust, fear, sadness, surprise, anticipation, joy and trust and the emotion lexicon developed by Mohammad & Turney 2013.
- social network analysis using different metrics such as most followed users, most befriended users, and betweenness centrality for identifying key players
- examination of differences in emotion and network between three major phases of the discussion using the Welch two sample t-test.
- news often coincide with changes in public sentiment
- Tweet posters and their relationships change considerably over time
- different influence metrics yield significantly different rankings