TY - GEN
T1 - Toward harmonizing self-reported and logged social data for understanding human behavior
AU - Singh, Vivek K.
AU - Jain, Arushi
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/5/2
Y1 - 2017/5/2
N2 - While self-reporting remains the most common method to understand human behavior, recent advances in social networks, mobile technologies, and other computermediated communication technologies are allowing researchers to obtain detailed logs of human behavior with ease. While the logged data is very useful (and accurate) at capturing the structure of the user's social network, the self-reported data provides an insight into the user's cognitive map of her social network. Based on a field study involving 47 users for a period of ten weeks we report that combining the two sets of data (self-reported and logged) gives higher predictive power than using either one of them individually. Further, the difference between the two types of values captures the level of dissonance between a user's actual and perceived social behavior and is found to be an important predictor of the person's social outcomes including social capital, social support and trust.
AB - While self-reporting remains the most common method to understand human behavior, recent advances in social networks, mobile technologies, and other computermediated communication technologies are allowing researchers to obtain detailed logs of human behavior with ease. While the logged data is very useful (and accurate) at capturing the structure of the user's social network, the self-reported data provides an insight into the user's cognitive map of her social network. Based on a field study involving 47 users for a period of ten weeks we report that combining the two sets of data (self-reported and logged) gives higher predictive power than using either one of them individually. Further, the difference between the two types of values captures the level of dissonance between a user's actual and perceived social behavior and is found to be an important predictor of the person's social outcomes including social capital, social support and trust.
KW - Bias
KW - Call-log data
KW - Dissonance coefficient
KW - Self-reported
KW - Social ties
KW - Socio-mobile behavior
UR - http://www.scopus.com/inward/record.url?scp=85044865602&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044865602&partnerID=8YFLogxK
U2 - 10.1145/3025453.3025856
DO - 10.1145/3025453.3025856
M3 - Conference contribution
AN - SCOPUS:85044865602
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 2233
EP - 2238
BT - CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
Y2 - 6 May 2017 through 11 May 2017
ER -