Publications

Online Social Networks (OSNs) are defined as a network of relationships where human entities represent the nodes and the edges between these nodes represent the relationships leading to online communities. Due to the rapid growth of social media networks and the consequent growth of communities on such networks, it was of great interest for researchers to study the evolution of such communities to know how they retain their members, attract new ones and grow over time. This paper focuses on the factors causing people to participate in certain communities on Twitter, which in turn affects the evolution of these communities. In order to study this evolution, a dataset of some Twitter hashtags related to career opportunities was collected for a period of two months between February and March 2016. The collected dataset allowed us to study the reciprocal effect of the users’ topological features and their activity levels. In this paper, three new measures are introduced (two influence measures and one topological measure). Those measures, in addition to the measures available in the literature, are used to spot the measures that can be used for influencing a user to attract other users to a certain hashtag. Focus is on the centrality and the activity level of participants and their effect on the activity level or the membership of other users on same communities.