Recommending Influenceable Targets Based on Influence Propagation Through Activity Behaviors in Online Social Media

Abstract

Online Social Media (OSM) is a platform through which the users present themselves to the connected world by means of messaging, posting, reacting, tagging, and sharing on different contents with also other social activities. Nowadays, it has a vast impact on various aspects of the industry, business and society along with on user’s life. In an OSN platform, reaching the target users is one of the primary focus for most of the businesses and other organizations. Identification and recommendation of influenceable targets help to capture the appropriate audience efficiently and effectively. In this paper, an effective model has been discussed in egocentric OSN by incorporating an efficient influence measured Recommendation System in order to generate a list of top most influenceable target users among all connected network members for any specific social network user. Firstly the list of interacted network members has been updated based on all activities. On which the interacted network members with most similar activities have been recommended based on the specific influence category with sentiment type. After that, the topmost influenceable network members in the basis of the required amount among those updated list of interacted network members have been identified with proper ranking by analyzing the similarity and frequency of their activity contents with respect to the activity contents of the main user. Through these two continuous stages, an effective list of top influenceable targets of the main user has been distinguished from the egocentric view of any social network.