TY - GEN
T1 - Who thinks who knows who? Socio-cognitive analysis of email networks
AU - Pathak, Nishith
AU - Mane, Sandeep
AU - Srivastava, Jaideep
PY - 2006
Y1 - 2006
N2 - Interpersonal interaction plays an important role in organizational dynamics, and understanding these interaction networks is a key issue for any organization, since these can be tapped to facilitate various organizational processes. However, the approaches of collecting data about them using surveys/interviews are fraught with problems of scalability, logistics and reporting biases, especially since such surveys may be perceived to be intrusive. Widespread use of computer networks for organizational communication provides a unique opportunity to overcome these difficulties and automatically map the organizational networks with a high degree of detail and accuracy. This paper describes an effective and scalable approach for modeling organizational networks by tapping into an organization's email communication. The approach models communication between actors as non-stationary Bernoulli trials and Bayesian inference is used for estimating model parameters over time. This approach is useful for sociocognitive analysis (who knows who knows who) of organizational communication networks. Using this approach, novel measures for analysis of (i) closeness between actors' perceptions about such organizational networks (agreement), (ii) divergence of an actor's perceptions about organizational network from reality (misperception) are explained. Using the Enron email data, we show that these techniques provide sociologists with a new tool to understand organizational networks.
AB - Interpersonal interaction plays an important role in organizational dynamics, and understanding these interaction networks is a key issue for any organization, since these can be tapped to facilitate various organizational processes. However, the approaches of collecting data about them using surveys/interviews are fraught with problems of scalability, logistics and reporting biases, especially since such surveys may be perceived to be intrusive. Widespread use of computer networks for organizational communication provides a unique opportunity to overcome these difficulties and automatically map the organizational networks with a high degree of detail and accuracy. This paper describes an effective and scalable approach for modeling organizational networks by tapping into an organization's email communication. The approach models communication between actors as non-stationary Bernoulli trials and Bayesian inference is used for estimating model parameters over time. This approach is useful for sociocognitive analysis (who knows who knows who) of organizational communication networks. Using this approach, novel measures for analysis of (i) closeness between actors' perceptions about such organizational networks (agreement), (ii) divergence of an actor's perceptions about organizational network from reality (misperception) are explained. Using the Enron email data, we show that these techniques provide sociologists with a new tool to understand organizational networks.
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U2 - 10.1109/ICDM.2006.168
DO - 10.1109/ICDM.2006.168
M3 - Conference contribution
AN - SCOPUS:84878047602
SN - 0769527019
SN - 9780769527017
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 466
EP - 477
BT - Proceedings - Sixth International Conference on Data Mining, ICDM 2006
T2 - 6th International Conference on Data Mining, ICDM 2006
Y2 - 18 December 2006 through 22 December 2006
ER -