Annotation and detection of conflict escalation in political debates

Kim, S., Valente, F. and Vinciarelli, A. (2013) Annotation and detection of conflict escalation in political debates. In: INTERSPEECH 2013: 14th Annual Conference of the International Speech Communication Association, Lyon, 25-29 August 2013,

Kim, S., Valente, F. and Vinciarelli, A. (2013) Annotation and detection of conflict escalation in political debates. In: INTERSPEECH 2013: 14th Annual Conference of the International Speech Communication Association, Lyon, 25-29 August 2013,

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Abstract

Conflict escalation in multi-party conversations refers to an increase in the intensity of conflict during conversations. Here we study annotation and detection of conflict escalation in broadcast political debates towards a machine-mediated conflict management system. In this regard, we label conflict escalation using crowd-sourced annotations and predict it with automatically extracted conversational and prosodic features. In particular, to annotate the conflict escalation we deploy two different strategies, i.e., indirect inference and direct assessment; the direct assessment method refers to a way that annotators watch and compare two consecutive clips during the annotation process, while the indirect inference method indicates that each clip is independently annotated with respect to the level of conflict then the level conflict escalation is inferred by comparing annotations of two consecutive clips. Empirical results with 792 pairs of consecutive clips in classifying three types of conflict escalation, i.e., escalation, de-escalation, and constant, show that labels from direct assessment yield higher classification performance (45.3% unweighted accuracy (UA)) than the one from indirect inference (39.7% UA), although the annotations from both methods are highly correlated (r�=0.74 in continuous values and 63% agreement in ternary classes).

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Vinciarelli, Professor Alessandro
Authors: Kim, S., Valente, F., and Vinciarelli, A.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Research Group:Social Signal Processing
Copyright Holders:Copyright © 2013 The Authors
Publisher Policy:Reproduced with the permission of the authors.
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
543881SSPNet - Social Signal Processing Network.Alessandro VinciarelliEuropean Commission (EC)UNSPECIFIEDCOM - COMPUTING SCIENCE