The computer for the 21st century
Human-computer interaction
Understanding and Using Context
Personal and Ubiquitous Computing
Using the Experience Sampling Method to Evaluate Ubicomp Applications
IEEE Pervasive Computing
Capturing experience: a matter of contextualising events
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
Experience sampling for building predictive user models: a comparative study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
International ethnographic observation of social networking sites
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Intelligibility and accountability: human considerations in context-aware systems
Human-Computer Interaction
Experiencing the Affective Diary
Personal and Ubiquitous Computing
Fancy a Drink in Canary Wharf?: A User Study on Location-Based Mobile Search
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part I
Motivating contributors in social media networks
WSM '09 Proceedings of the first SIGMM workshop on Social media
Place-Its: a study of location-based reminders on mobile phones
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
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Location-aware messages left by people can make visible some aspects of their everyday experiences at a location. To understand the contextual factors surrounding how users produce and consume location-aware multimedia messaging (LMM), we use an experience-centered framework that makes explicit the different aspects of an experience. Using this framework, we conducted an exploratory, diary study aimed at eliciting implications for the study and design of LMM systems. In an earlier pilot study, we found that subjects did not have enough time to fully capture their everyday experiences using an LMM prototype, which led us to conduct a longer study using a multimodal diary method. The diary study data (verified for reliability using a categorization task) provided a closer look at the different aspects (spatiotemporal, social, affective, and cognitive) of people's experience. From the data, we derive three main findings (predominant LMM domains and tasks, capturing experience vs. experience of capture, context-dependent personalization) to inform the study and design of future LMM systems.