Introducing fuzzy like in social networks and its effects on advertising profits and human behavior

Today, over two billion and 300 million people are using social networks. Advertisement in social networks is a multibillion dollar business and it is projected that by the year 2019 social networks will have over 70-billion-dollars income annually only from advertisement. On the other hand, showing relevant advertisements to social network users, can increase this revenue. In this paper, we have reviewed different methods for showing relevant advertisement to social network members. Furthermore, a novel concept called fuzzy like in social networks has been proposed and based on that, two types of algorithms and implementation are introduced: explicit fuzzy like and implicit fuzzy like. Implicit fuzzy like algorithm uses mouse interaction of users to implicitly realize their like level and show relevant advertisements to them. We have validated this algorithm by performing a two-step experiment on advertisement in a social network. Our study results show that implementing fuzzy like in social networks can improve social networks revenue from advertisement. Besides, we have studied fuzzy like effects on human behavior, using explicit fuzzy like algorithm by implementing fuzzy like bar in social network users' profile pages. Experiment results show that men are more likely to use fuzzy like than women and fuzzy like is less popular with young people and it is most popular with adults and old people.