TY - GEN
T1 - Modeling influence in online multi-party discourse
AU - Shaikh, Samira
AU - Strzalkowski, Tomek
AU - Stromer-Galley, Jenny
AU - Broadwell, George Aaron
AU - Taylor, Sarah
AU - Liu, Ting
AU - Ravishankar, Veena
AU - Ren, Xiaoai
AU - Boz, Umit
PY - 2012
Y1 - 2012
N2 - In this article, we present our novel approach towards the detection and modeling of complex social phenomena in multi-party discourse, including leadership, influence, pursuit of power and group cohesion. We have developed a two-tier approach that relies on observable and computable linguistic features of conversational text to make predictions about sociolinguistic behaviors such as Topic Control and Disagreement, that speakers deploy in order to achieve and maintain certain positions and roles in a group. These sociolinguistic behaviors are then used to infer higher-level social phenomena such as Influence, which is the focus of this paper. We show robust performance results by comparing our computational results to participants' own perceptions and rankings of influence. We use weights learnt from correlations with known influence rankings to compute and score sociolinguistic behaviors and show performance significantly above baseline for two data sets and two different languages.
AB - In this article, we present our novel approach towards the detection and modeling of complex social phenomena in multi-party discourse, including leadership, influence, pursuit of power and group cohesion. We have developed a two-tier approach that relies on observable and computable linguistic features of conversational text to make predictions about sociolinguistic behaviors such as Topic Control and Disagreement, that speakers deploy in order to achieve and maintain certain positions and roles in a group. These sociolinguistic behaviors are then used to infer higher-level social phenomena such as Influence, which is the focus of this paper. We show robust performance results by comparing our computational results to participants' own perceptions and rankings of influence. We use weights learnt from correlations with known influence rankings to compute and score sociolinguistic behaviors and show performance significantly above baseline for two data sets and two different languages.
KW - computational socio-linguistics
KW - influence
KW - linguistic behavior
KW - multi-disciplinary artificial intelligence
KW - online dialogues
KW - social computing
KW - social phenomena
UR - http://www.scopus.com/inward/record.url?scp=84874635840&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874635840&partnerID=8YFLogxK
U2 - 10.1109/CGC.2012.94
DO - 10.1109/CGC.2012.94
M3 - Conference contribution
AN - SCOPUS:84874635840
SN - 9780769548647
T3 - Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012
SP - 515
EP - 522
BT - Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012
T2 - 2nd International Conference on Cloud and Green Computing, CGC 2012, Held Jointly with the 2nd International Conference on Social Computing and Its Applications, SCA 2012
Y2 - 1 November 2012 through 3 November 2012
ER -