Simulating Audiences: Automating Analysis of Values, Attitudes, and Sentiment

TitleSimulating Audiences: Automating Analysis of Values, Attitudes, and Sentiment
Publication TypeConference Papers
Year of Publication2011
AuthorsTempleton TC, Fleischmann KR, Boyd-Graber J
Conference NamePrivacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom)
Date Published2011/10/09/11
ISBN Number978-1-4577-1931-8
Keywordsaudience simulation, behavioural sciences computing, crowdsourcing, Educational institutions, human values, HUMANS, learning (artificial intelligence), machine learning, moral argument, natural language processing, Park51 project, Presses, public controversy, public discussion, Security, social sciences computing, support vector machines, Weaving

Current events such as the Park51 Project in downtown Manhattan create "critical discourse moments," explosions of discourse around a topic that can be exploited for data gathering. Policymakers have a need to understand the dynamics of public discussion in real time. Human values, which are cognitively related to attitudes and serve as reference points in moral argument, are important indicators of what's at stake in a public controversy. This work shows that it is possible to link values data with reader behavior to infer values implicit in a topical corpus, and that it is possible to automate this process using machine learning.