Launcher nodes for detecting efficient influencers in social networks

• Published in: Online social networks and media Vol. 25; p. 100157
• Main Authors: Martins, Pedro, Martins, Filipa Alarcão
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: Influence propagation; Influencers; Message viral power; Social networks; Influencers; Social networks; Influence propagation; Message viral power
• ISSN: 2468-6964; 2468-6964
• DOI: 10.1016/j.osnem.2021.100157

Influence propagation in social networks is a subject of growing interest. A relevant issue in those networks involves the identification of key influencers. These players have an important role on viral marketing strategies and message propagation, including political propaganda and fake news. In effect, an important way to fight malicious usage on social networks is to understand their properties, their structure and the way messages propagate. This paper proposes a new index for analyzing message propagation in social networks, based on the network topological nature and the influential power of the message. The new index characterizes the strength of each node as a launcher of the message, dividing the nodes into launchers and non-launchers. This division is most evident when the viral power of the message is high. Together with other known metrics, launcher individuals can assist to select efficient influencers in a social network. For instance, instead of choosing a strong member according to its degree in the network (number of followers), we may previously select those belonging to the launchers group and then look for the lowest degree members contained therein. These members are probably cheaper (on financial incentives) but still guarantying almost the same influence effectiveness as the largest degree members. We discuss this index using a number of real-world social networks available in known datasets repositories. •We propose a new index for message propagation, identifying launchers.•We also propose classifying the nodes by their influence efficiency.•Strong launchers with low degree are key efficient influencers.•It uses just the topological information of the graph, being easy to apply in practice.