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Social networks have become an important information hub with a huge customer base. Hence businesses are trying to leverage social networks in their advertising and marketing campaigns. One of the advantages of social networks is that users can influence their community and friends. Detecting influential users provides multiple benefits for businesses such as more effective advertisement and better user involvement. Many techniques, focusing on network centrality metrics, have been developed to detect influential users. However, these techniques do handle spammers and fake friends effectively and may even return inaccurate results. Moreover, social network content can also be influential. Knowing who can influence who and in what area is important in advertisement and sale prediction. The effect of influence increases when influential content is distributed by influential users. We aim to provide a solution to accurately detect influential users by integrating sociometric techniques with topic-based influence models while solving the shortcomings of both techniques.
Dr. Morad Benyoucef
Amir Afrasiabi Rad
Information and communications technologies
University of Ottawa
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