Using Estimated Personality of Social Network Members for Finding Influential Nodes in Viral Marketing

Abstract

Sending promotional messages to a few numbers of users in a social network can propagate a product through word of mouth. However, choosing users that receive promotional messages, in order to maximize propagation, is a considerable issue. These recipients are named ''influential nodes.'' To recognize influential nodes, according to the literature, criteria such as the relationships of network members or information shared by each member on a social network have been used. One of the effective factors in diffusion of messages is the personality characteristics of members. As far as we know, although this issue is considerable, so far it has not been applied in the previous studies. In this article, using the graph structure of social networks, two personality characteristics, openness and extroversion, are estimated for network members. Next, these two estimated characteristics together with other characteristics of social networks, are considered as the criteria of choosing influential nodes. To implement this process, the real coded genetic algorithm is used. The proposed method has been evaluated on a dataset including 1000 members of Twitter. Our results indicate that using the proposed method, compared with simple heuristic methods, can improve performance up to 37%.

Letting the Computers Take Over: Using AI to Solve Marketing Problems

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Identification of multi-spreader users in social networks for viral marketing

“…Traditional methods in which the companies get directly involved in advertisement are not applicable and efficient anymore [1]. Accordingly, they resort to other methods such as viral marketing [1–8]. In this method, which is carried out on social network platforms, the advertisement starts from an initial set of people whom the company encourages to cooperate by giving them discounts, offering them free goods or presenting useful information [1, 5, 7, 9–11].…”

The Influences of Personality and Motivation on the Sharing of Misinformation on Social Media

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