# abstract
Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. This data can be used to better understand people, such as their personality traits, perceptions, and preferences, and predict their behavior. This deeper understanding of users and their behaviors can benefit a wide range of intelligent applications, such as advertising, social recommender systems, and personalized knowledge management. These applications will also benefit individual users themselves by optimizing their experiences across a wide variety of domains, such as retail, healthcare, and education. Since mining and understanding user behavior from social media often requires interdisciplinary effort, including machine learning, text mining, human-computer interaction, and social science, our workshop aims to bring together researchers and practitioners from multiple fields to discuss the creation of deeper models of individual users by mining the content that they publish and the social networking behavior that they exhibit.  2012 Authors.

# publicationOutlet
Proceedings of the 21st ACM international conference on Information and knowledge management

# nameOfConference
Proceedings of the 21st ACM international conference on Information and knowledge management

# publisher.label
Association for Computing Machinery (ACM)

# authorList[]
Mahmud, J., Caverlee, J., Nichols, J., Donovan, J. O., & Zhou, M.

# editorList[]
Chen, X., Lebanon, G., Wang, H., & Zaki, M. J.