Analysis model for measuring information flow in social networks

Recently, as the need to advertise using social networks has increased, viral marketing has become one of the most effective approaches used to spread information, in a virus-like manner, to a large number of people. This approach is able to match customers and products more efficiently than when using traditional marketing approaches. However, the complex social structure of the digital network means it is difficult to assess the actual performance of such information sharing. This paper proposes the use of an analysis model in order to measure information flows across social networks. Many factors affect the performance of information flows across social networks, as they depend, not only on the number of communications required to reach the target audience, but also on the individual and social parameters used by the target audience, such as the number of friends who may be interested in the target product and their preferences. This study assesses the importance of those parameters affecting customer product awareness.