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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.

Footnote

Dazu eine Korrektur in: Journal of the Association for Information Science and Technology 67(2016) no.7, S.1588-1606. "On page 1594 of the above-mentioned article, the sentence "a sentiment polarity (positive [+1] or negative [-1]) and is categorized into one (or more) emotional category." should be "Each word was assigned a sentiment polarity (positive [+1] or negative [-1]) and is categorized into one (or more) emotional category. The sentiment polarity of a tweet is the average the sentiment polarities of all words in the tweet.""

Source

Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1588-1606

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Date

22. 3.2009 17:57:50

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.595-607

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Abstract

As the Web is used increasingly to share and disseminate information, business analysts and managers are challenged to understand stakeholder relationships. Traditional stakeholder theories and frameworks employ a manual approach to analysis and do not scale up to accommodate the rapid growth of the Web. Unfortunately, existing business intelligence (BI) tools lack analysis capability, and research on BI systems is sparse. This research proposes a framework for designing BI systems to identify and to classify stakeholders on the Web, incorporating human knowledge and machine-learned information from Web pages. Based on the framework, we have developed a prototype called Business Stakeholder Analyzer (BSA) that helps managers and analysts to identify and to classify their stakeholders on the Web. Results from our experiment involving algorithm comparison, feature comparison, and a user study showed that the system achieved better within-class accuracies in widespread stakeholder types such as partner/sponsor/supplier and media/reviewer, and was more efficient than human classification. The student and practitioner subjects in our user study strongly agreed that such a system would save analysts' time and help to identify and classify stakeholders. This research contributes to a better understanding of how to integrate information technology with stakeholder theory, and enriches the knowledge base of BI system design.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.1, S.59-74

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Source

Journal of the American Society for Information Science and Technology. 59(2008) no.8, S.1347-1359

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Source

Journal of the American Society for Information Science and Technology. 55(2004) no.9, S.818-831

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Source

Journal of the American Society for Information Science and Technology. 55(2004) no.10, S.873-891