Name
Abstract | ||
|---|---|---|
Online marketing exploits social influence to trigger chain-like cascades. However, recent practices actively employ agents to collaboratively inflate the spreading of influences. Through supporting structures, they help each other with false feedback and signals to attract other users in the spreading process and thus alter the spontaneous social dynamics. In this paper, we proposed a modeling framework to explain the mechanism of such operations and characterize the spreading dynamics. Model analytics and numerical simulations both showed a lifting in overall spreading influence. As empirical evidence, experiments on a large Weibo network revealed well-structured advertising groups that prominently amplified the influences of promoted commercials via meticulous cooperation in a core-peripheral structure. The inflation effect also brings new considerations into influence maximization problems. Based on our models, we solved the problem of maximizing inflated influence by optimizing the selection of agents under KKT conditions and their supporting structure using its submodular property. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ASONAM.2014.6921622 | ASONAM |
Keywords | Field | DocType |
optimisation,core peripheral structure,social dynamics,weibo network,numerical analysis,advertising groups,maximization problems,marketing data processing,model analytics,influence inflation,internet,spreading dynamics,false signals,online social networks,online marketing,meticulous cooperation,social networking (online),numerical simulations,false feedback,spreading process,mathematical model,concrete,collaboration | Data mining,Social network,Computer science,Submodular set function,Online advertising,Social influence,Social dynamics,Artificial intelligence,Complex network,Analytics,Karush–Kuhn–Tucker conditions,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4799-5876-4 | 1 | 0.36 |
References | Authors | |
22 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianjun Xie | 1 | 1 | 1.04 |
| Chuang Zhang | 2 | 13 | 3.32 |
| Ming Wu | 3 | 1 | 0.70 |
| Yun Huang | 4 | 49 | 5.83 |