DUBMMSM'12: international workshop on data-driven user behavioral modeling and mining from social media

J. Mahmud, James Caverlee, Jeffrey Nichols, J. O'Donovan, Michelle X. Zhou
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引用次数: 1

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.
社交媒体数据驱动的用户行为建模与挖掘国际研讨会[j]
Twitter和Facebook等社交媒体网站正在产生大量数据。这些数据可以用来更好地了解人们,比如他们的个性特征、观念和偏好,并预测他们的行为。这种对用户及其行为的更深入的理解可以使广泛的智能应用受益,例如广告、社交推荐系统和个性化知识管理。通过优化零售、医疗保健和教育等广泛领域的体验,这些应用程序还将使个人用户本身受益。由于从社交媒体中挖掘和理解用户行为通常需要跨学科的努力,包括机器学习、文本挖掘、人机交互和社会科学,我们的研讨会旨在汇集来自多个领域的研究人员和实践者,通过挖掘他们发布的内容和他们展示的社交网络行为来讨论个人用户的更深层次模型的创建。
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