Participant and observer attribution of affect in interpersonal conflict: An examination of noncontent verbal behavior

Cooper, Virginia W.

doi:10.1007/bf01000009

Cited by 19 publications

(17 citation statements)

References 22 publications

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“…Furthermore, the main effect of emotions in conflict seems to be the choice of strategies (e.g., avoidance vs. engagement) that can correspond to different behavioral displays [13], [14]. Therefore, this work privileges social signals that, following the literature, tend to be less ambiguous as conflict markers [3], [15], [16], [17], [18].…”

Section: Introductionmentioning

confidence: 92%

Predicting Continuous Conflict Perceptionwith Bayesian Gaussian Processes

Kim

Valente

Filippone

et al. 2014

IEEE Trans. Affective Comput.

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Abstract-Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational social signals (loudness, overlapping speech, etc.) and predicts the conflict level perceived by human observers in continuous, non-categorical terms. The proposed regression approach is fully Bayesian and it adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception.

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Section: Introductionmentioning

confidence: 92%

Predicting Continuous Conflict Perceptionwith Bayesian Gaussian Processes

Kim

Valente

Filippone

et al. 2014

IEEE Trans. Affective Comput.

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“…The physical layer includes the behavioral cues observed during conflicts and include interruptions, overlapping speech, cues related to turn organization in conversations as well as head nodding, fidgeting and frowning [5]. The inferential layer is based on the perception of the competitive processes occurring in conversations where conflict is viewed as a 'mode of interaction' governed by the principle that "the attainment of the goal by one party precludes its attainment by the others" [2,15].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Our aim is to capture facial behavioral cues that are deemed intrinsically correlated with conflict, such as smiling, blinking, head nodding, flouncing and frowning [5,14]. Both shape-and appearance-based descriptors are examined.…”

Section: Featuresmentioning

confidence: 99%

“…Various research studies in human sciences argue that a "disagreement" does not have to result in a conflict; conflict describes a high level of disagreement, or "escalation of disagreement", where at least one of the involved interlocutors feels emotionally offended. Similarly to other phenomena arising in social interactions [3,4], conflict is largely manifested by means of non-verbal behavioral cues including facial expressions, body postures, gestures, and head movements, as well as conversational social signals including interruptions, overlapping speech, loudness and other cues associated with turn-organization [5]. Conflict, which has been recognized as one of the main dimensions along which a dyadic or multi-party social interaction is perceived, is usually accompanied by negative effects on communication and social life [6].…”

Section: Introductionmentioning

confidence: 99%

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The Conflict Escalation Resolution (CONFER) Database

Georgakis

Panagakis

Zafeiriou

et al. 2017

Image and Vision Computing

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Conflict is usually defined as a high level of disagreement taking place when individuals act on incompatible goals, interests, or intentions. Research in human sciences has recognized conflict as one of the main dimensions along which an interaction is perceived and assessed. Hence, automatic estimation of conflict intensity in naturalistic conversations would be a valuable tool for the advancement of human-centered computing and the deployment of novel applications for social skills enhancement including conflict management and negotiation. However, machine analysis of conflict is still limited to just a few works, partially due to an overall lack of suitable annotated data, while it has been mostly approached as a conflict or (dis)agreement detection problem based on audio features only. In this work, we aim to overcome the aforementioned limitations by a) presenting the Conflict Escalation Resolution (CONFER) Database, a set of excerpts from audio-visual recordings of televised political debates where conflicts naturally arise, and b) reporting baseline experiments on audio-visual conflict intensity estimation. The database contains approximately 142 minutes of recordings in Greek language, split over 120 non-overlapping episodes of naturalistic conversations that involve two or three interactants. Subject-and session-independent experiments are conducted on continuous-time (frame-by-frame) estimation of real-valued conflict intensity, as opposed to binary conflict/non-conflict clas- * Corresponding author. E-mail address: christos.georgakis@imperial.ac.uk Preprint submitted to Image and Vision Computing December 20, 2016 A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTsification. For the problem at hand, the efficiency of various audio and visual features and fusion of them as well as various regression frameworks is examined. Experimental results suggest that there is much room for improvement in the design and development of automated multi-modal approaches to continuous conflict analysis. The CONFER Database is publicly available for non-commercial use at http://ibug.doc.ic.ac.uk/resources/confer/.

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“…Since the work aims at identifying nonverbal markers of conflict, the questions of the physical layer focus on how frequent are a number of nonverbal behavioural cues (interruptions, fidgeting, frown, etc.). In particular, the questionnaire has focused on those cues that are most typically associated with competition and conflict such as the attempts of preventing others from speaking, the degree of motor activation and facial expressions conveying disappointment and aggresiveness [11].…”

Section: The Annotationmentioning

confidence: 99%

Collecting data for socially intelligent surveillance and monitoring approaches: The case of conflict in competitive conversations

Vinciarelli

Kim

Valente

et al. 2012

2012 5th International Symposium on Communications, Control and Signal Processing

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One of the most recent trends in surveillance research is the application of socially aware approaches, i.e. approaches that integrate human sciences findings in order to better understand, model and predict the behaviour of people under observation. One of the key requirements for the development of such approaches is the collection of corpora that provide sufficient and reliable information about social phenomena of interest. However, the computing community still pays relatively little attention to the application of methodologies suitable for observational data collection, possibly inspired by human sciences experimental work. This paper tries to fill, at least partially, such a gap by providing an introduction to data collection techniques applied in nonverbal behaviour research. The collection of a corpus aimed at the study of conflict in conversations is used as a case study and example.

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Participant and observer attribution of affect in interpersonal conflict: An examination of noncontent verbal behavior

Cited by 19 publications

References 22 publications

Predicting Continuous Conflict Perceptionwith Bayesian Gaussian Processes

Predicting Continuous Conflict Perceptionwith Bayesian Gaussian Processes

The Conflict Escalation Resolution (CONFER) Database

Collecting data for socially intelligent surveillance and monitoring approaches: The case of conflict in competitive conversations

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