Modeling Dynamics of Task and Social Cohesion from the Group Perspective Using Nonverbal Motion Capture-based Features

Walocha, Fabian; Maman, Lucien; Chétouani, Mohamed; Varni, Giovanna

doi:10.1145/3395035.3425963

Cited by 12 publications

(7 citation statements)

References 40 publications

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“…Finally, projects have started to enable the (semi-)automatic measurement of social cohesion within small groups based on non-verbal social cues, such as gaze, head nods, laughter and turn taking (Hung and Gatica-Perez 2010; Kantharaju, Langlet, Barange et al 2020; Walocha, Maman, Chetouani et al 2020).…”

Section: Computer Vision In Social Sciencementioning

confidence: 99%

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Bernasco

Hoeben

Koelma

et al. 2022

Sociological Methods & Research

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Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and running the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical challenges.

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Section: Computer Vision In Social Sciencementioning

confidence: 99%

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Bernasco

Hoeben

Koelma

et al. 2022

Sociological Methods & Research

View full text Add to dashboard Cite

show abstract

“…Finally, projects have started to enable the (semi-)automatic measurement of social cohesion within small groups based on non-verbal social cues, such as gaze, head nods, laughter and turn taking (Hung and Gatica-Perez 2010, Kantharaju, Langlet, Barange et al 2020, Walocha, Maman, Chetouani et al 2020).…”

Section: Social Signal Processingmentioning

confidence: 99%

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Bernasco¹,

Hoeben²,

Koelma³

et al. 2021

Preprint

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Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and run the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical limitations.

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“…Walocha et al, proposed a method based on notions from cooperative game theory (i.e., using SHAP values) to assess the importance of motion capture-based features on a random forest model predicting the dynamics of the Social and Task dimensions of cohesion. Interplay is faced through a multilabel classification [49]. Their model, however, addresses cohesion using a single modality only.…”

Section: Related Workmentioning

confidence: 99%

“…body movement and gesture). Indeed, both play an important role in nonverbal communication [21] and is relevant for predicting Social and Task dynamics of cohesion [49]. Regarding the audio data, we adopt features from the Geneva Minimalistic Acoustic Parameter Set (GeMAPS; see [15] for a detailed description) that is composed of features related to frequency, energy, spectral balance and temporal features of the voice.…”

Section: Individual and Group Featuresmentioning

confidence: 99%

Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict its Dynamics Leveraging Social Sciences

Maman

Likforman-Sulem

Chétouani

et al. 2021

Proceedings of the 2021 International Conference on Multimodal Interaction

Self Cite

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Emergent states are behavioral, cognitive and affective processes appearing among the members of a group when they interact together. In the last decade, the development of computational approaches received a growing interest in building Human-Centered systems. Such a development is particularly difficult because some of these states have several dimensions interplaying somehow and somewhere over time. In this paper, we focus on cohesion, its dimensions and their interplay. Several definitions of cohesion exist, it can be simply defined as the tendency of a group to stick together to pursue goals and/or affective needs. This plethora of definitions resulted in many different cohesion dimensions. Social and Task dimensions are the most investigated both in Social Sciences and Computer Science since they both play an important role in a wide range of contexts and groups. To the best of our knowledge, however, no previous work on the prediction of cohesion dynamics focused on how these 2 dimensions interplay. We leverage Social Sciences to address this issue. In particular, we take advantage of the importance of Social cohesion for creating flexible and constructive relationships to reinforce Task cohesion. We describe a Deep Neural Network architecture (DNN) for predicting the dynamics of Task cohesion by applying transfer learning from a pre-trained model dedicated to the prediction of Social cohesion dynamics. Our architecture is evaluated against several baselines. Results show that it significantly improves the predictions of the Task cohesion dynamics, confirming the benefits of integrating Social Sciences insights into models architectures. CCS CONCEPTS• Human-centered computing → Collaborative and social computing; • Computing methodologies → Artificial intelligence.

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Modeling Dynamics of Task and Social Cohesion from the Group Perspective Using Nonverbal Motion Capture-based Features

Cited by 12 publications

References 40 publications

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict its Dynamics Leveraging Social Sciences

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