Personalized ambient environments using multiple social networks

Personality plays an important role in the personalized experience of ambient environments. Several aspects of ambient environments, such as advertisement and marketing, can adapt according to individual user´s personality. Several works have been published that seek to acquire various personality traits by analyzing Internet usage statistics. Researchers have used Facebook, Twitter, YouTube, and various other nostalgic websites to collect usage statistics. However, we are still far from a successful outcome. In this paper, we use a range of divergent features of the Facebook and LinkedIn social networks, both separately and collectively, in order to achieve better results. Our experimental results show that the accuracy of personality detection improves with the use of complementary features of multiple social networks. To the best of our knowledge, this is the first work on detecting personality from multiple social networks. Furthermore, this is also the first attempt to detect personality from the LinkedIn social network in an automated manner.