Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict Its Dynamics Leveraging Social Sciences. Maman, L., Likforman-Sulem, L., Chetouani, M., & Varni, G. In Proceedings of the 2021 International Conference on Multimodal Interaction, of ICMI '21, pages 16–24, New York, NY, USA, 2021. Association for Computing Machinery. Paper Paper abstract bibtex 14 downloads 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.
@inproceedings{maman-2021-icmi,
author = {Maman, Lucien and Likforman-Sulem, Laurence and Chetouani, Mohamed and Varni, Giovanna},
title = {Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict Its Dynamics Leveraging Social Sciences},
year = {2021},
isbn = {9781450384810},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = { 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.},
booktitle = {Proceedings of the 2021 International Conference on Multimodal Interaction},
pages = {16–24},
numpages = {9},
keywords = {Group Dynamics, Multimodal Interaction, Cohesion, Social Signal Processing, Transfer Learning},
location = {Montr\'{e}al, QC, Canada},
series = {ICMI '21},
url = {https://doi.org/10.1145/3462244.3479940},
url_Paper = {https://lucienmaman.github.io/files/ICMI2021_nocop.pdf}
}
Downloads: 14
{"_id":"Qd5eLT94hG8yLPMkL","bibbaseid":"maman-likformansulem-chetouani-varni-exploitingtheinterplaybetweensocialandtaskdimensionsofcohesiontopredictitsdynamicsleveragingsocialsciences-2021","author_short":["Maman, L.","Likforman-Sulem, L.","Chetouani, M.","Varni, G."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Maman"],"firstnames":["Lucien"],"suffixes":[]},{"propositions":[],"lastnames":["Likforman-Sulem"],"firstnames":["Laurence"],"suffixes":[]},{"propositions":[],"lastnames":["Chetouani"],"firstnames":["Mohamed"],"suffixes":[]},{"propositions":[],"lastnames":["Varni"],"firstnames":["Giovanna"],"suffixes":[]}],"title":"Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict Its Dynamics Leveraging Social Sciences","year":"2021","isbn":"9781450384810","publisher":"Association for Computing Machinery","address":"New York, NY, USA","abstract":"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.","booktitle":"Proceedings of the 2021 International Conference on Multimodal Interaction","pages":"16–24","numpages":"9","keywords":"Group Dynamics, Multimodal Interaction, Cohesion, Social Signal Processing, Transfer Learning","location":"Montréal, QC, Canada","series":"ICMI '21","url":"https://doi.org/10.1145/3462244.3479940","url_paper":"https://lucienmaman.github.io/files/ICMI2021_nocop.pdf","bibtex":"@inproceedings{maman-2021-icmi,\nauthor = {Maman, Lucien and Likforman-Sulem, Laurence and Chetouani, Mohamed and Varni, Giovanna},\ntitle = {Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict Its Dynamics Leveraging Social Sciences},\nyear = {2021},\nisbn = {9781450384810},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nabstract = { Emergent states are behavioral, cognitive and affective processes appearing among\nthe members of a group when they interact together. In the last decade, the development\nof computational approaches received a growing interest in building Human-Centered\nsystems. Such a development is particularly difficult because some of these states\nhave several dimensions interplaying somehow and somewhere over time. In this paper,\nwe focus on cohesion, its dimensions and their interplay. Several definitions of cohesion\nexist, it can be simply defined as the tendency of a group to stick together to pursue\ngoals and/or affective needs. This plethora of definitions resulted in many different\ncohesion dimensions. Social and Task dimensions are the most investigated both in\nSocial Sciences and Computer Science since they both play an important role in a wide\nrange of contexts and groups. To the best of our knowledge, however, no previous work\non the prediction of cohesion dynamics focused on how these 2 dimensions interplay.\nWe leverage Social Sciences to address this issue. In particular, we take advantage\nof the importance of Social cohesion for creating flexible and constructive relationships\nto reinforce Task cohesion. We describe a Deep Neural Network architecture (DNN) for\npredicting the dynamics of Task cohesion by applying transfer learning from a pre-trained\nmodel dedicated to the prediction of Social cohesion dynamics. Our architecture is\nevaluated against several baselines. Results show that it significantly improves the\npredictions of the Task cohesion dynamics, confirming the benefits of integrating\nSocial Sciences insights into models architectures.},\nbooktitle = {Proceedings of the 2021 International Conference on Multimodal Interaction},\npages = {16–24},\nnumpages = {9},\nkeywords = {Group Dynamics, Multimodal Interaction, Cohesion, Social Signal Processing, Transfer Learning},\nlocation = {Montr\\'{e}al, QC, Canada},\nseries = {ICMI '21},\nurl = {https://doi.org/10.1145/3462244.3479940},\nurl_Paper = {https://lucienmaman.github.io/files/ICMI2021_nocop.pdf}\n}\n\n","author_short":["Maman, L.","Likforman-Sulem, L.","Chetouani, M.","Varni, G."],"key":"maman-2021-icmi","id":"maman-2021-icmi","bibbaseid":"maman-likformansulem-chetouani-varni-exploitingtheinterplaybetweensocialandtaskdimensionsofcohesiontopredictitsdynamicsleveragingsocialsciences-2021","role":"author","urls":{"Paper":"https://doi.org/10.1145/3462244.3479940"," paper":"https://lucienmaman.github.io/files/ICMI2021_nocop.pdf"},"keyword":["Group Dynamics","Multimodal Interaction","Cohesion","Social Signal Processing","Transfer Learning"],"metadata":{"authorlinks":{}},"downloads":14,"html":""},"bibtype":"inproceedings","biburl":"https://lucienmaman.github.io/publications/my_publications_bibbase.bib","dataSources":["qHuuHpohSBrTjkfwF"],"keywords":["group dynamics","multimodal interaction","cohesion","social signal processing","transfer learning"],"search_terms":["exploiting","interplay","between","social","task","dimensions","cohesion","predict","dynamics","leveraging","social","sciences","maman","likforman-sulem","chetouani","varni"],"title":"Exploiting the Interplay between Social and Task Dimensions of Cohesion to Predict Its Dynamics Leveraging Social Sciences","year":2021,"downloads":14}