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Lecture Notes in Computer Science
Human Activity Recognition (HAR) using social media provides a solid basis for a variety of context-aware applications. Existing HAR approaches have adopted supervised machine learning algorithms using texts and their meta-data such as time, venue, and keywords. However, their recognition accuracy may decrease when applied to imagesharing social media where users mostly describe their daily activities and thoughts using both texts and images. In this paper, we propose a semi-superviseddoi:10.1007/978-3-030-47426-3_67 fatcat:wqnltmtwencbrdvamml5cm4fqe