The exchange of information is an innate and natural process that assist in content dispersal.Social networking sites emerge to enrich their users by providing the facility for sharing information and social interacti...The exchange of information is an innate and natural process that assist in content dispersal.Social networking sites emerge to enrich their users by providing the facility for sharing information and social interaction.The extensive adoption of social networking sites also resulted in user content generation.There are diverse research areas explored by the researchers to investigate the influence of social media on users and confirmed that social media sites have a significant impact on markets,politics and social life.Facebook is extensively used platform to share information,thoughts and opinions through posts and comments.The identification of influential users on the social web has grown as hot research field because of vast applications in diverse areas for instance political campaigns marketing,e-commerce,commercial and,etc.Prior research studies either uses linguistic content or graph-based representation of social network for the detection of influential users.In this article,we incorporate association rule mining algorithms to identify the top influential users through frequent patterns.The association rules have been computed using the standard evaluation measures such as support,confidence,lift,and conviction.To verify the results,we also involve conventional metrics for example accuracy,precision,recall and F1-measure according to the association rules perspective.The detailed experiments are carried out using the benchmark College-Msg dataset extracted by Facebook.The obtained results validate the quality and visibility of the proposed approach.The outcome of propose model verify that the association rule mining is able to generate rules to identify the temporal influential users on Facebook who are consistent on regular basis.The preparation of rule set help to create knowledge-based systems which are efficient and widely used in recent era for decision making to solve real-world problems.展开更多
Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote bo...Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding this work through Research Group No.RG-21-51-01.
文摘The exchange of information is an innate and natural process that assist in content dispersal.Social networking sites emerge to enrich their users by providing the facility for sharing information and social interaction.The extensive adoption of social networking sites also resulted in user content generation.There are diverse research areas explored by the researchers to investigate the influence of social media on users and confirmed that social media sites have a significant impact on markets,politics and social life.Facebook is extensively used platform to share information,thoughts and opinions through posts and comments.The identification of influential users on the social web has grown as hot research field because of vast applications in diverse areas for instance political campaigns marketing,e-commerce,commercial and,etc.Prior research studies either uses linguistic content or graph-based representation of social network for the detection of influential users.In this article,we incorporate association rule mining algorithms to identify the top influential users through frequent patterns.The association rules have been computed using the standard evaluation measures such as support,confidence,lift,and conviction.To verify the results,we also involve conventional metrics for example accuracy,precision,recall and F1-measure according to the association rules perspective.The detailed experiments are carried out using the benchmark College-Msg dataset extracted by Facebook.The obtained results validate the quality and visibility of the proposed approach.The outcome of propose model verify that the association rule mining is able to generate rules to identify the temporal influential users on Facebook who are consistent on regular basis.The preparation of rule set help to create knowledge-based systems which are efficient and widely used in recent era for decision making to solve real-world problems.
文摘Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.
