ABSTRACT
Building consensus is a process in which participants start with various opinions and reach an agreement as far as possible. During a discussion, participants exhibit different stances such as agree or disagree at other's utterances. In this paper, a stance analysis of participants by combining BBS tree structure and participant relation graph is proposed. The stance change is measured by an approach of information theory. Finally, simulations are provided to demonstrate the feasibility of the proposed method.
- Lyness A L, Raimond J A. 1992. Electronic communication to promote consensus-building skills: an innovative teaching strategy. The Journal of nursing education, 31(7): 331.Google Scholar
- Cline R J W. 1990. Detecting groupthink: Methods for observing the illusion of unanimity. Communication Quarterly, 1990, 38(2): 112--126.Google ScholarCross Ref
- Cabrerizo F J, Pérez I J, Herrera-Viedma E. 2010. Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information. Knowledge-Based Systems, 2010, 23(2): 169--181. Google ScholarDigital Library
- McGann, Anthony J. 2006. The Logic of Democracy: Reconciling, Equality, Deliberation, and Minority Protection, University of Michigan Press.Google Scholar
- Olfati-Saber R, Murray R M. 2004. Consensus problems in networks of agents with switching topology and time-delays. Automatic Control, IEEE Transactions, 2004, 49(9): 1520--1533.Google Scholar
- Chen L, Frolik J. 2011. Self-aware distributed consensus building for sensor networks. ISRN Communications and Networking, 2011: 34. Google ScholarDigital Library
- Chen L, Frolik J. 2012. Active consensus over sensor networks via selective communication. Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2012 9th Annual IEEE Communications Society Conference on. IEEE,: 389--397.Google ScholarCross Ref
- He N, Oda Y, Otani C, et al. 2012. Consensus building analysis using entropy in BBS tree. Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services. ACM, 2012: 315--318. Google ScholarDigital Library
- Murakami A, Raymond R. 2010. Support or oppose?: classifying positions in online debates from reply activities and opinion expressions. Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Association for Computational Linguistics, 2010: 869--875. Google ScholarDigital Library
- Shigemasu K and Ueno M. 1993A new item response model with parameters reflecting state of knowledge. Behaviormetrika, 1993: 20(2): pp.161--169.Google ScholarCross Ref
- Galley M, McKeown K, Hirschberg J, et al. 2004. Identifying agreement and disagreement in conversational speech: Use of bayesian networks to model pragmatic dependencies. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2004: 669. Google ScholarDigital Library
Index Terms
- Conversation Analysis Based on Interpersonal Relationship in Consensus Building
Recommendations
A two-step approach to building bilateral consensus between agents based on relationship learning theory
Researchers are increasingly focusing on the agent based approach to transaction support in ubiquitous commerce. These agents work autonomously to maximize utility on the user's behalf. In the case of a cooperative game, rather than a win-lose zero-sum ...
A Study on Consensus Building Mechanism Based on Kansei ~Consideration of Experimental Tasks that Cause Conflicts~
Human-Computer Interaction. Theoretical Approaches and Design MethodsAbstractWe are engaged in a variety of consensus-building activities in our daily lives. In order to build consensus, it is necessary to reach a single conclusion unanimously. Therefore, if there are conflicting opinions, they need to be resolved. To deal ...
A New Approach to Better Consensus Building and Agreement Implementation for Trustworthy AI Systems
Computer Safety, Reliability, and Security. SAFECOMP 2021 WorkshopsAbstractWe propose a system that focuses on consensus building and agreement implementation as the basis for establishing AI trustworthiness. Our approach is new, and we have called it consensus building with an assurance case: it is based on applying ...
Comments