Identifying Twitter Users Influence and Open Mindedness Using Anomaly Detection

Mario Alfonso Prado-Romero, Alberto Fernández Oliva, Lucina García Hernández

Abstract: Social networks help us to connect and share our thoughts with family and friends. Businesses want to take advantage of social media to better reach their customers, but traditional advertising results annoying for most social network users. As a result, the use of influencers to help a message reach their target audience has become a topic of great interest. Despite the many works in this field, detecting influence in social networks is still an open topic. In this work we propose to use anomaly detection for finding “influential” and “open minded” individuals in the Twitter network. Targeting these users can help advertisers to reach closed communities and to increase the spread of their message.