DUBMOD14 - International Workshop on Data-driven User Behavioral Modeling and Mining from Social Media Conference Paper uri icon


  • Copyright 2014 ACM. Massive amounts of data are be ing generated on social media sites, such as Twitter and Facebook. These data can be used to better understand people (e.g., personality traits, perceptions, and preferences) and predict their behavior. As a result, a deeper understanding of users and their behavior 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 them selves and optimize their experience 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.

author list (cited authors)

  • Mahmud, J., Nichols, J., Zhou, M., Caverlee, J., Zeng, Y. i., Chen, L., & O'Donovan, J.

citation count

  • 0

editor list (cited editors)

  • Li, J., Wang, X. S., Garofalakis, M. N., Soboroff, I., Suel, T., & Wang, M.

publication date

  • November 2014


  • ACM  Publisher