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• W2060118520 abstract "Boredom is a common human emotion which may lead to an active search for stimulation. People often turn to their mobile phones to seek that stimulation. In this paper, we tackle the challenge of automatically inferring boredom from mobile phone usage. In a two-week in-the-wild study, we collected over 40,000,000 usage logs and 4398 boredom self-reports of 54 mobile phone users. We show that a user-independent machine-learning model of boredom --leveraging features related to recency of communication, usage intensity, time of day, and demographics-- can infer boredom with an accuracy (AUCROC) of up to 82.9%. Results from a second field study with 16 participants suggest that people are more likely to engage with recommended content when they are bored, as inferred by our boredom-detection model. These findings enable boredom-triggered proactive recommender systems that attune their users' level of attention and need for stimulation." @default.
• W2060118520 created "2016-06-24" @default.
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• W2060118520 date "2015-09-07" @default.
• W2060118520 modified "2023-10-03" @default.
• W2060118520 title "When attention is not scarce - detecting boredom from mobile phone usage" @default.
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• W2060118520 doi "https://doi.org/10.1145/2750858.2804252" @default.
• W2060118520 hasPublicationYear "2015" @default.
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