Using the influence model to recognize functional roles in meetings

Dong, Wen; Lepri, Bruno; Cappelletti, Alessandro; Pentland, Alex; Pianesi, Fabio; Zancanaro, Massimo

doi:10.1145/1322192.1322239

Cited by 92 publications

(88 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, researchers have used the influence model in understanding the functional role (follower, orienteer, giver, seeker, etc.) of each individual in a mission survival group discussion dataset [17]. By using the reality mining [10] cellphone sensor data from 80 MIT members as observations, and constraining the latent space of each individual to be binary "work" and "home", researchers found that the influence matrix gleaned from this data matches well with the organizational relationship between individuals [13].…”

Section: Social Influence In Human Societymentioning

confidence: 99%

“…The influence strength between two nodes learned by our model can be then treated as tie weights in social networks.This key contribution connects the conditional probabilistic dependence to a weighted network topology. In fact, in previous works, the most common usage for the influence model is to use R to understand social structure [13,14,17,18]. …”

Section: Appendix A: Latent State Influence Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Society's Nervous System: Building Effective Government, Energy, and Public Health Systems

Pentland¹

2012

Computer

View full text Add to dashboard Cite

Drawing on a unique, multi-year collaboration with the heads of major IT, wireless, hardware, health, and financial firms, as well as the heads of American, EU, and other regulatory organizations, and a variety of NGOs [1,2],I describe the potential for pervasive and mobile sensing and computing over the next decade, and the challenges that will have to be faced in order to realize this potential. Building Effective Systems for SocietyHow can we design the systems and networks needed for a healthy, sustainable society? This problem is not new: in the 1800's, the industrial revolution spurred the growth of cities and created huge health problems. The solution then was to engineer centralized networks that delivered clean water and safe food, removed waste, provided energy, facilitated transportation, and provided access to centralized healthcare, police, and educational services.

show abstract

Section: Social Influence In Human Societymentioning

confidence: 99%

Section: Appendix A: Latent State Influence Modelingmentioning

confidence: 99%

Society's Nervous System: Building Effective Government, Energy, and Public Health Systems

Pentland¹

2012

Computer

View full text Add to dashboard Cite

show abstract

“…Pianesi et al [39,40] estimate personality traits, specifically extraversion (sociable, assertive, playful) versus intraversion (aloof, reserved, shy) using Support Vector Regression and applied to sequences of the MS (Mission Survival) Corpus. Using an influence model, Dong et al [9] estimated functional roles in meetings related to tasks and socio-emotional roles on the MS Corpus. The work by Lepri et al [28] estimated individual performance from interaction slices.…”

Section: Human Behavior Analysis Using Infrastructurebased Sensorsmentioning

confidence: 99%

Privacy-sensitive recognition of group conversational context with sociometers

et al. 2011

View full text Add to dashboard Cite

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%.

show abstract

“…Social roles are useful to characterize the dynamics of the conversation, i.e., the interaction between the participants, they can generalize across any type of conversation and can be related to phenomena studied in meetings like engagement, hot-spots [11] and also social dominance. Previous works on social roles have mainly used non-lexical features [12,13,14] focusing on how participants interact over long time windows (up to one minute) in the conversation, with the exception of [15] where information capturing speaker expressiveness, derived from lexical and prosodic features, was used over short time windows.…”

Section: Introductionmentioning

confidence: 99%

“…The literature on the first has mainly focused on the use of lexical and structural features [3,5,6,7] while the literature on the second has mainly made use of non-lexical information [12,13,14] (prosody and turn-taking statistics). This work aims at studying in exhaustive way and on the same dataset which of the various features proposed in literature are able to capture information on speaker formal/social roles.…”

Section: Introductionmentioning

confidence: 99%

Automatic speaker role labeling in AMI meetings: Recognition of formal and social roles

Sapru

Valente

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

This work aims at investigating the automatic recognition of speaker role in meeting conversations from the AMI corpus. Two types of roles are considered: formal roles, fixed over the meeting duration and recognized at recording level, and social roles related to the way participants interact between themselves, recognized at speaker turn level. Various structural, lexical and prosodic features as well as Dialog Act tags are exhaustively investigated and combined for this purpose. Results reveal an accuracy of 74% in recognizing the speakers formal roles and an accuracy of 66% (percentage of time) in correctly labeling the social roles. Feature analysis reveals that lexical features provide the higher performances in formal/functional role recognition while prosodic features provide the higher performances in social role recognition. Furthermore results reveal that social role recognition in case of rare roles in the corpus can be improved through the use of lexical and Dialog Act information combined over short time windows.

show abstract

Using the influence model to recognize functional roles in meetings

Cited by 92 publications

References 13 publications

Society's Nervous System: Building Effective Government, Energy, and Public Health Systems

Society's Nervous System: Building Effective Government, Energy, and Public Health Systems

Privacy-sensitive recognition of group conversational context with sociometers

Automatic speaker role labeling in AMI meetings: Recognition of formal and social roles

Contact Info

Product

Resources

About