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![RC visualizing though CI oscillations over time [17] This figure illustrates Rotating Contribution (RC) for two teams, one with high RC, and one with low RC. Actors are placed along the Y-axis, while the X-axis encodes time, and the Z-axis the Contribution Index (CI) of actors for each hour, sorted, each hour, by the decreasing CI of actors. The back plane, which rises and falls, represents the set of actors who rotate taking the lead as most vocal contributors. The left picture illustrates an example of a team with high RC; RC oscillates highly among time steps and the actors of the team. This example was drawn from a 6-day long graduate student seminar, and communications were measured using sociometric badges. This image includes 15 actors, and has had](profile/Peter-Gloor/publication/263620050/figure/fig2/AS:296048686845956@1447594915273/RC-visualizing-though-CI-oscillations-over-time-17-This-figure-illustrates-Rotating.png)
RC visualizing though CI oscillations over time [17] This figure illustrates Rotating Contribution (RC) for two teams, one with high RC, and one with low RC. Actors are placed along the Y-axis, while the X-axis encodes time, and the Z-axis the Contribution Index (CI) of actors for each hour, sorted, each hour, by the decreasing CI of actors. The back plane, which rises and falls, represents the set of actors who rotate taking the lead as most vocal contributors. The left picture illustrates an example of a team with high RC; RC oscillates highly among time steps and the actors of the team. This example was drawn from a 6-day long graduate student seminar, and communications were measured using sociometric badges. This image includes 15 actors, and has had
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![Figure 1: RL visualized through oscillations in BC over time [17] This...](profile/Peter-Gloor/publication/263620050/figure/fig1/AS:296048686845953@1447594915182/RL-visualized-through-oscillations-in-BC-over-time-17-This-figure-illustrates-Rotating_Q320.jpg)
![Figure 2: RC visualizing though CI oscillations over time [17] This...](profile/Peter-Gloor/publication/263620050/figure/fig2/AS:296048686845956@1447594915273/RC-visualizing-though-CI-oscillations-over-time-17-This-figure-illustrates-Rotating_Q320.jpg)
Research into human dynamical systems has long sought to identify robust
signals for human behavior. We have discovered a series of social network-based
indicators that are reliable predictors of team creativity and collaborative
innovation. We extract these signals from electronic records of interpersonal
interactions, including e-mail, and face-t...
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Most research of online communication focuses on modes of communication that
are either open (like forums, bulletin boards, Twitter, etc.) or direct (like
e-mails). In this work, we study a dataset that has both types of communication
channels. We relate our findings to theories of social organization and human
dynamics. The data comprises 36,492 u...
Citations
... Numerous studies in various domains have attempted to explain what makes groups work together effectively. Approaches in the area of human-computer interaction have focused on social signal processing to understand the impact of communication channels 2 and 3 on various team outcomes such as creativity [8] or performance [16]. However, previously published studies on the effect of social signals on team outcomes are not consistent, identifying various signals as potentially relevant. ...
While meetings take up a significant part of the workday, participants often perceive them as poor and unproductive. With the surge in videoconferencing meetings for work due to the COVID-19 pandemic, many employees experienced that videoconferencing can even aggravate negative experiences in meetings. Past research has shown that the level of engagement during meetings is a crucial aspect of meeting a success. While there have been some attempts towards utilizing post-analysis feedback, there is little effort towards real-time support to improve engagement. This research explores the development of a visual support system for automated, real-time feedback on team communication behavior during online meetings. We present a novel, fully working visual support system that was evaluated with positive results. This study outlines the step-wise development of the method. We collected a range of qualitative feedback measures to understand better how users perceive the visual support system. First, we collected qualitative feedback from participants and eye-tracking data (n = 4) to evaluate four visualization approaches. The second step evaluated the best-performing visualization by a user study with participants (N = 72) working in groups of four on a collaborative problem-solving task. Users give the tool good scores on a seven-point Likert scale: perceived usefulness (4.8), ease of understanding (5.6), and perceived precision (5.1). Our results indicate that our novel system can enhance the quality of video conferencing through real-time visual support.
... While early work for data-driven modeling on collaboration behavior patterns has mainly aimed to model lower-level behavioral dimensions, such as turntaking [31], recent efforts go beyond low-level signals to model high-level collaborative behavioral patterns. For example, postural markers have been used in human activity recognition to differentiate team member group functions [6], and proxemic features have been shown to be indicators of knowledge-sharing dynamics and affect group creativity [17,12]. ...
... This is particularly applicable to colocated collaboration settings because face-to-face teamwork remains the dominant mode for solving complex problems despite the increase of virtual teams. Furthermore, colocated collaboration provides unique benefits that are not easy to achieve in digitally mediated forms of teamwork [28], such as increasing creativity [12] and performance [25]. While preliminary work has demonstrated the feasibility and utility of leveraging multimodal signals to predict behavioral patterns during collaboration activities, more research is needed to understand which data sources work best to predict certain activities. ...
Despite the importance of team communication for successful collaborative problem solving, automated solutions for teams are notably absent from the literature. One promising avenue of research has been the development and integration of speech-based technology for team meetings. However, these technologies often fall short of meeting the needs of the teams as they do not take meeting context into consideration. In this paper, we demonstrate the efficacy of context detection with data collected during real team meetings. By capturing and analyzing social signals of rotation in team dynamics, we can demonstrate that different stages of collaborative problem solving using the design thinking methodology differ in their dynamics. Using supervised machine learning, we successfully predict design thinking mode with an overall F1 score of 0.68 and a best-performing sub-class model of 0.94. We believe this to be an essential step towards improving speech-based technology that aims to assist teams during meetings. Making these automated systems context-aware will enable them to provide teams with relevant information, such as resources or guidance.
KeywordsSocial signalsContext detectionPredictive modeling
... actor to respond to the email messages he/she receives, or to tweets which are directed at him (Ego ART). A second measure -Alter ART -measures the time it takes for others to respond to a user or to tweets where a user is mentioned (Gloor, Almozlino, Inbar, Lo, & Provost, 2014). ...
... Nudges. This variable counts, on average, the number of pings (nudges) required before a social actor answers to an email or to a tweet directed to him (Gloor et al., 2014). Nudges can be subsequent emails sent to an employee who has not yet responded, or new tweets which keep mentioning a Twitter user before receiving an answer. ...
This paper investigates the research question if senders of large amounts of irrelevant or unsolicited information - commonly called "spammers" - distort the network structure of social networks. Two large social networks are analyzed, the first extracted from the Twitter discourse about a big telecommunication company, and the second obtained from three years of email communication of 200 managers working for a large multinational company. This work compares network robustness and the stability of centrality and interaction metrics, as well as the use of language, after removing spammers and the most and least connected nodes. The results show that spammers do not significantly alter the structure of the information-carrying network, for most of the social indicators. The authors additionally investigate the correlation between e-mail subject line and content by tracking language sentiment, emotionality, and complexity, addressing the cases where collecting email bodies is not permitted for privacy reasons. The findings extend the research about robustness and stability of social networks metrics, after the application of graph simplification strategies. The results have practical implication for network analysts and for those company managers who rely on network analytics (applied to company emails and social media data) to support their decision-making processes.
... employee to answer to forum posts directed to him/her (Ego ART); we also considered the symmetrical measure -the Alter ART -which represents the time taken by the other users to answer to his/her messages (Gloor, Almozlino, Inbar, Lo, & Provost, 2014). ...
... Nudges. This measure counts the average number of pings, i.e. nudges required before a social actor answers to a message (Gloor et al., 2014). Nudges can be subsequent messages sent to a user which has not yet answered. ...
In this study, we tested the robustness of three communication networks extracted from the online forums included in the intranet platforms of three large companies. For each company we analyzed the communication among employees both in terms of network structure and content (language used). Over a period of eight months, we analyzed more than 52,000 messages posted by approximately 12,000 employees. Specifically, we tested the network robustness and the stability of a set of structural and semantic metrics, while applying several different node removal strategies. We removed the forum moderators, the spammers, the overly connected nodes and the nodes lying at the network periphery, also testing different combinations of these selections. Results indicate that removing spammers and very peripheral nodes can be a relatively low impact strategy in this context; accordingly, it could be used to clean the noise generated by these types of social actor and to reduce the computation complexity of the analysis. On the other hand, the removal of moderators seems to have a significant impact on the network connectivity and the shared content. The most affected variables are closeness centrality and contribution index. We also found that the removal of overly connected nodes can significantly change the network structure. Lastly, we compared the behavior of moderators with the other users, finding distinctive characteristics by which moderators can be identified when their list is unknown. Our findings can help online community managers to understand the role of moderators within intranet forums and can be useful for social network analysts who are interested in evaluating the effects of graph simplification techniques.
... In this study, we explore possible cues in the managers' communication behavior that indicate a change in the relationship "managers-organization" and possibly a fracture in the psychological contract. Following a method similar to the embeddedness approach to turnover (Mitchell, Holtom, Lee, Sablynski, & Erez, 2001) we used new social network metrics such as betweenness centrality oscillation, average response time, nudges and emotionality metrics (Allen, Gloor, Fronzetti Colladon, Woerner, & Raz, 2016;Gloor, Almozlino, Inbar, & Provost, 2014) to identify changes in the communication behaviors of managers who are close to quit their job. ...
In this study we propose a method based on e-mail social network analysis to compare the communication behavior of managers who voluntarily quit their job and managers who decide to stay. Collecting 18 months of e-mail, we analyzed the communication behavior of 866 managers, out of which 111 left a large global service company. We compared differences in communication patterns by computing social network metrics, such as betweenness and closeness centrality, and content analysis indicators, such as emotionality and complexity of the language used. To study the emergence of managers' disengagement, we made a distinction based on the period of e-mail data examined. We observed communications during months 5 and 4 before managers left, and found significant variations in both their network structure and use of language. Results indicate that on average managers who quit had lower closeness centrality and less engaged conversations. In addition, managers who chose to quit tended to shift their communication behavior starting from 5 months before leaving, by increasing their degree and closeness centrality, the complexity of their language, as well as their oscillations in betweenness centrality and the number of "nudges" they need to send to peers before getting an answer.
... This measure evaluates the average time it takes a social actor to respond to the email messages he/she receives, or to tweets which are directed at him (Ego ART). A second measure -Alter ART -measures the time it takes for others to respond to a user or to tweets where a user is mentioned (Gloor, Almozlino, Inbar, Lo, & Provost, 2014). ...
... Nudges. This variable counts, on average, the number of pings (nudges) required before a social actor answers to an email or to a tweet directed to him (Gloor et al., 2014). Nudges can be subsequent emails sent to an employee who has not yet responded, or new tweets which keep mentioning a Twitter user before receiving an answer. ...
This paper investigates the research question if senders of large amounts of irrelevant or unsolicited information – commonly called “spammers” – distort the network structure of social networks. Two large social networks are analyzed, the first extracted from the Twitter discourse about a big telecommunication company, and the second obtained from three years of email communication of 200 managers working for a large multinational company. This work compares network robustness and the stability of centrality and interaction metrics, as well as the use of language, after removing spammers and the most and least connected nodes. The results show that spammers do not significantly alter the structure of the information-carrying network, for most of the social indicators. The authors additionally investigate the correlation between e-mail subject line and content by tracking language sentiment, emotionality, and complexity, addressing the cases where collecting email bodies is not permitted for privacy reasons. The findings extend the research about robustness and stability of social networks metrics, after the application of graph simplification strategies. The results have practical implication for network analysts and for those company managers who rely on network analytics (applied to company emails and social media data) to support their decision-making processes.
... Woolley et al.'s (2010) article showing that task groups with conversational dominants are less collectively intelligent than those with balanced speech participation has been the most prominent and influential publication to date. Other studies connect social signaling with creativity (e.g., Gloor et al. 2011Gloor et al. , 2012Gloor et al. , 2014Tripathi and Burleson 2012) or with business outcomes (e.g., Olguín et al. 2009;Orbach et al. 2014), while some ask how bodily energetics relate to flow states, 10 how sociometric analytics improve task group management (Gloor et al. 2013;Kim et al. 2008), and how speech participation and physical proximity differ by gender (Onnela et al. 2014). Researchers hoping to use sociometers for practical applications have begun to test their validity and reliability. ...
... Most disturbingly, a sociometer would sometimes simply fail to collect data. We are not the first to report this (Gloor et al. 2014;Tripathi and Burleson 2012). We estimate that the odds of failure were approximately 1 in 20 sensors and appeared to increase with the length of the data collection session or the number of actors involved in the interaction. ...
New technologies transform research specialties and potentiate new fields. Sociometers—wearable electronic sensors collecting quantitative, dynamic data about embodied social interactions at hyperfine scales—represent such a possibility for small group research. This article introduces this new method and its distinctive qualities and affordances. Next, we relate its potential for advancing understanding of three types of small group phenomena: (1) rhythmic entrainment, emotional energy, and solidarity; (2) nonverbal dominance and deference; and (3) groups as complex systems. We then present findings from two pilot studies of small group creativity in science and art collaborations. To do so, we combine sociometric, survey, and ethnographic data to consider how speech participation, body movement, and volume shape group creativity and to illustrate how sociometric data complements traditional qualitative and quantitative methods. We close by relating practical lessons learned to aid future researchers working to harness this powerful new research technology.
... Average Response Time (ART): This variable measures the average time taken by an employee to answer to forum posts directed to him/her ( Ego ART ); we also considered the symmetrical measure -the Alter ART -which represents the time taken by the other users to answer to his/her messages ( Gloor, Almozlino, Inbar, Lo, & Provost, 2014 ). ...
... Nudge s: This measure counts the average number of pings, i.e. nudges required before a social actor answers to a message ( Gloor et al., 2014 ). Nudges can be subsequent messages sent to a user which has not yet answered. ...
In this study, we tested the robustness of three communication networks extracted from the online forums included in the intranet platforms of three large companies. For each company we analyzed the communication among employees both in terms of network structure and content (language used). Over a period of eight months, we analyzed more than 52,000 messages posted by approximately 12,000 employees. Specifically, we tested the network robustness and the stability of a set of structural and semantic metrics, while applying several different node removal strategies. We removed the forum moderators, the spammers, the overly connected nodes and the nodes lying at the network periphery, also testing different combinations of these selections. Results indicate that removing spammers and very peripheral nodes can be a relatively low impact strategy in this context; accordingly, it could be used to clean the “noise” generated by these types of social actor and to reduce the computation complexity of the analysis. On the other hand, the removal of moderators seems to have a significant impact on the network connectivity and the shared content. The most affected variables are closeness centrality and contribution index. We also found that the removal of overly connected nodes can significantly change the network structure. Lastly, we compared the behavior of moderators with the other users, finding distinctive characteristics by which moderators can be identified when their list is unknown. Our findings can help online community managers to understand the role of moderators within intranet forums and can be useful for social network analysts who are interested in evaluating the effects of graph simplification techniques.
... In the other hand, [9] proposed Indicators based on social networks that are predictors of team creativity and collaborative innovation. They presented three indicators Rotary Leadership (RL) & Contribution (RL), and prompt response time (PRT). ...
... Finding the optimal level of group-creativity may require plasticity-rigidity alterations of social groups. In agreement with this assumption, an intermediate amount of long-range connections (Guimera et al, 2005;Shore et al, 2015) resulting in the simultaneous presence of boundary spanning brokerage and trust-building closure (Tortoriello & Krackhardt, 2010;Uribe & Wang, 2014), as well as rotating leadership and contribution (Gloor et al, 2014) were shown to be key factors of teamsuccess in business, arts, sports and science. Changes of group-plasticity and rigidity dominance may be an important learning mechanism of social groups as detailed below. ...
Network support is a key success factor for talented people. As an example, the Hungarian Talent Support Network involves close to 1500 Talent Points and more than 200,000 people. This network started the Hungarian Templeton Program identifying and helping 315 exceptional cognitive talents. This network is a part of the European Talent Support Network initiated by the European Council for High Ability involving more than 300 organizations in over 30 countries in Europe and extending in other continents. These networks are giving good examples that talented people often occupy a central, but highly dynamic position in social networks. The involvement of such 'creative nodes' in network-related decision making processes is vital, especially in novel environmental challenges. Such adaptive/learning responses characterize a large variety of complex systems from proteins, through brains to society. It is crucial for talent support programs to use these networking and learning processes to increase their efficiency further.
