Inferring private information using social network data
Proceedings of the 18th international conference on World wide web
Using twitter to recommend real-time topical news
Proceedings of the third ACM conference on Recommender systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Modeling people's place naming preferences in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Classifying latent user attributes in twitter
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. People from all walks of life share data about social events, express opinions, discuss their interests, publicize businesses, recommend products, and, explicitly or implicitly, reveal personal information. This workshop will focus on the use of social media data for creating models of individual users from the content that they publish. Deeper understanding of user behavior and associated attributes can benefit a wide range of intelligent applications, such as social recommender systems and expert finders, as well as provide the foundation in support of novel user interfaces (e.g., actively engaging the crowd in mixed-initiative question-answering systems). These applications and interfaces may offer significant benefits to users across a wide variety of domains, such as retail, government, healthcare and education. User modeling from public social media data may also reveal information that users would prefer to keep private. Such concerns are particularly important because individuals do not have complete control over the information they share about themselves. For example, friends of a user may inadvertently divulge private information about that user in their own posts. In this workshop we will also discuss possible mechanisms that users might employ to monitor what information has been revealed about themselves on social media and obfuscate any sensitive information that has been accidentally revealed.