Identifying emergent leadership in small groups using nonverbal communicative cues

Sanchez-Cortes, Dairazalia; Aran, Oya; Mast, Marianne Schmid; Gática-Pérez, Daniel

doi:10.1145/1891903.1891953

Cited by 45 publications

(40 citation statements)

References 11 publications

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“…This trait highly correlates with dominance and the Big Five personality traits [7]. In addition, Sanchez-Cortes et al in [10] demonstrate that correlations exist between the perception of leadership and automatically extracted visual cues.…”

Section: Prediction Of Personality Traitsmentioning

confidence: 99%

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Multi-domain and multi-task prediction of extraversion and leadership from meeting videos

Kındıroğlu

Akarun

Aran

2017

J Image Video Proc.

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Automatic prediction of personalities from meeting videos is a classical machine learning problem. Psychologists define personality traits as uncorrelated long-term characteristics of human beings. However, human annotations of personality traits introduce cultural and cognitive bias. In this study, we present methods to automatically predict emergent leadership and personality traits in the group meeting videos of the Emergent LEAdership corpus. Prediction of extraversion has attracted the attention of psychologists as it is able to explain a wide range of behaviors, predict performance, and assess risk. Prediction of emergent leadership, on the other hand, is of great importance for the business community. Therefore, we focus on the prediction of extraversion and leadership since these traits are also strongly manifested in a meeting scenario through the extracted features. We use feature analysis and multi-task learning methods in conjunction with the non-verbal features and crowd-sourced annotations from the Video bLOG (VLOG) corpus to perform a multi-domain and multi-task prediction of personality traits. Our results indicate that multi-task learning methods using 10 personality annotations as tasks and with a transfer from two different datasets from different domains improve the overall recognition performance. Preventing negative transfer by using a forward task selection scheme yields the best recognition results with 74.5% accuracy in leadership and 81.3% accuracy in extraversion traits. These results demonstrate the presence of annotation bias as well as the benefit of transferring information from weakly similar domains.

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Section: Prediction Of Personality Traitsmentioning

confidence: 99%

“…These studies use the abundance of personal data from different domains such as short essays, blog posts [21], social media profiles [18], surveillance with wearable sensors [22,23], computer game behavior [24] and videos containing interaction between people [10,17,20].…”

Section: Prediction Of Personality Traitsmentioning

confidence: 99%

Multi-domain and multi-task prediction of extraversion and leadership from meeting videos

Kındıroğlu

Akarun

Aran

2017

J Image Video Proc.

Self Cite

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“…Ad hoc roles were estimated using Dynamic Bayesian Networks (DBN) and turn-taking information in broadcast video by Vinciarelli [43]; and in AMI corpus by [15]. Recently, Sanchez-Cortes et al [42] modeled emergent leadership using turn-taking patterns and employing score-level fusion techniques. Unlike the above works, Basu et al [1] using a dynamic Bayesian approach, in an unsupervised approach, estimated pair-wise influence between participants in a group.…”

Section: Human Behavior Analysis Using Infrastructurebased Sensorsmentioning

confidence: 99%

Privacy-sensitive recognition of group conversational context with sociometers

et al. 2011

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Recognizing the conversational context in which group interactions unfold has applications in machines that support collaborative work and perform automatic social inference using contextual knowledge. This paper addresses the task of discriminating one conversational context from another, specifically brainstorming from decision-making interactions, using easily computable nonverbal behavioral cues. Privacy-sensitive mobile sociometers are used to record the interaction data. We hypothesize that the difference in the conversational dynamics between brainstorming and decision-making discussions is significant and measurable using speaking activity-based nonverbal cues. We characterize the communication patterns of the entire group by the aggregation (both temporal and person-wise) of their nonverbal behavior. The results on our interaction data set show that the floor-occupation patterns in a brainstorming interaction are different from a decision-making interaction, and our method can obtain a classification accuracy as high as 87.5%.

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“…Recent advances in recording equipment [5] and signal processing may ultimately enable automated and realtime analysis of talking mannerisms and social interactions at large, yielding more objective results. Several studies in that direction have been conducted in recent years, to deduce individual characteristics like dominance status [6][7], emerging leadership [8] and other personality related traits [9][10]. In such studies, various statistics of the conversation are extracted, e.g., natural turns, turn duration, speaking percentage, interruptions and failed interruptions.…”

Section: Introductionmentioning

confidence: 99%

“…However, in none of those studies [6][7][8][9][10][11], social interactions are analysed in real-time, instead various corpora of audio and video recordings are analysed offline [12]. Many corpora of audio and video signals are available related to small-group interactions (see [13] for a survey).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Feedback System for Monitoring and Facilitating Discussions

Sarda

Constable

Dauwels

et al. 2013

Natural Interaction With Robots, Knowbots and Smartphones

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Abstract-In this paper we present a system that provides realtime feedback about an ongoing discussion. Various speech statistics, such as speaking length, speaker turns, and speaking turn duration, are computed and displayed in real-time. In social monitoring, such statistics have been used to interpret and deduce talking mannerisms of people, and gain insights on human social characteristics and behaviour. However, such analysis is usually conducted in an offline fashion, after the discussion has ended. In contrast, our system analyses the speakers and provides feedback to the speakers in real-time during the discussion, which is a novel approach with plenty of potential applications. The proposed system consists of portable, easy to use equipment for recording the conversations. A user friendly graphical user interface displays statistics about the ongoing discussion. Customized individual feedback to participants during conversation can be provided. Such close-loop design may help individuals to contribute effectively in the group discussion, potentially leading to more productive and perhaps shorter meetings. Here we present preliminary results on two-people face to face discussion. In the longer term, our system may prove to be useful, e.g., for coaching purposes, and for facilitating business meetings.

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Identifying emergent leadership in small groups using nonverbal communicative cues

Cited by 45 publications

References 11 publications

Multi-domain and multi-task prediction of extraversion and leadership from meeting videos

Multi-domain and multi-task prediction of extraversion and leadership from meeting videos

Privacy-sensitive recognition of group conversational context with sociometers

Real-Time Feedback System for Monitoring and Facilitating Discussions

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