Innovative data mining techniques and applications in social networks

• Κύριος συγγραφέας: Βικάτος, Παντελεήμων
• Άλλοι συγγραφείς: Μακρής, Χρήστος
• Μορφή: Thesis
• Γλώσσα: English
• Έκδοση: 2018
• Θέματα: Data mining; Social media marketing; Εξόρυξη δεδομένων; Κοινωνικά δίκτυα; 006.312

Nowadays, people constantly use online social networking sites sharing content about their daily lives and things that happen around them. These systems have revolutionized the way we communicate, by organizing our offline social relationships in a digital form. Simultaneously, the social media serve the intentions of marketers to promote their brands and products due to this massive participation in these platforms and the endless potentials of improving their strategies for more effective marketing campaigns. The selection of the targets, the diffusion of brand promotion messages and the place of advertising content in popular web pages constitute some of most significant objectives of marketers in order to gain the interest of potential customers. Our research objective is to deal marketing campaign tasks using data mining techniques. We explore new methodologies in marketing campaign targeting, conversational and personalized advertising as well as the information propagation in Online Social Networks. We introduce a methodology for calculating user influence and select targets of marketing campaigns using bridge participation in the evolving social graph. We analyze the improvement of social bots infiltration using automated communication skills and we introduce the conversational social bots as advertising content promoters that improve brand engagement. We provide a novel methodology for personalized advertising using hotlink assignment. Our method enhances browsing experience and leads users to certain advertising content through hotlinks. We also introduce a novel methodology to achieve information diffusion within a social graph that activates a realistic number of users. Our approach combines the predicted patterns of diffusion for each node with propagation heuristics in order to achieve an effective cover of the graph. Our methodologies are useful to recommendation systems as well as to marketers who are interested to use data mining techniques to run effective marketing campaigns.