TY - GEN
T1 - Understanding Information Diffusion under Interactions
AU - Su, Yuan
AU - Zhang, Xi
AU - Yu, Philip S.
AU - Hua, Wen
AU - Zhou, Xiaofang
AU - Fang, Binxing
N1 - Funding Information:
This work was supported in part by State Key Development Program of Basic Research of China (No. 2013CB329605), the Natural Science Foundation of China (No. 61300014, 61372191, 61472263), China Scholarship Council, the Australian Research Council (Grants No. DP150103008) and NSF through grants III-1526499.
PY - 2016/7
Y1 - 2016/7
N2 - Information diffusion in online social networks has attracted substantial research effort. Although recent models begin to incorporate interactions among contagions, they still don't consider the comprehensive interactions involving users and contagions as a whole. Moreover, the interactions obtained in previous work are modeled as latent factors and thus are difficult to understand and interpret. In this paper, we investigate the contagion adoption behavior by incorporating various types of interactions into a coherent model, and propose a novel interaction-aware diffusion framework called IAD. IAD exploits the social network structures to distinguish user roles, and uses both structures and texts to categorize contagions. Experiments with large-scale Weibo dataset demonstrate that IAD outperforms the state-of-art baselines in terms of F1-score and accuracy, as well as the runtime for learning. In addition, the interactions obtained through learning reveal interesting findings, e.g., food-related contagions have the strongest capability to suppress other contagions' propagation, while advertisement-related contagions have the weakest capability.
AB - Information diffusion in online social networks has attracted substantial research effort. Although recent models begin to incorporate interactions among contagions, they still don't consider the comprehensive interactions involving users and contagions as a whole. Moreover, the interactions obtained in previous work are modeled as latent factors and thus are difficult to understand and interpret. In this paper, we investigate the contagion adoption behavior by incorporating various types of interactions into a coherent model, and propose a novel interaction-aware diffusion framework called IAD. IAD exploits the social network structures to distinguish user roles, and uses both structures and texts to categorize contagions. Experiments with large-scale Weibo dataset demonstrate that IAD outperforms the state-of-art baselines in terms of F1-score and accuracy, as well as the runtime for learning. In addition, the interactions obtained through learning reveal interesting findings, e.g., food-related contagions have the strongest capability to suppress other contagions' propagation, while advertisement-related contagions have the weakest capability.
UR - http://www.scopus.com/inward/record.url?scp=85006172878&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85006172878
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 3875
EP - 3881
BT - IJCAI 2016 - Proceedings of the 25th International Joint Conference on Artificial Intelligence
T2 - 25th International Joint Conference on Artificial Intelligence, IJCAI 2016
Y2 - 9 July 2016 through 15 July 2016
ER -