Social-media-based public policy informatics: Sentiment and network analyses of U.S. Immigration and border security

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.

Summary

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.

Method

1. a framework for collecting relevant Tweets from Twitter
2. 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.
3. social network analysis using different metrics such as most followed users, most befriended users, and betweenness centrality for identifying key players
4. examination of differences in emotion and network between three major phases of the discussion using the Welch two sample t-test.

Conclusions

1. news often coincide with changes in public sentiment
2. Tweet posters and their relationships change considerably over time
3. different influence metrics yield significantly different rankings