In the field of social network, fast detection of the burst topic plays a decisive role in emergency response and disposal. However, social data are noisy and sparse, which evolves with time going on and space changing make it difficult to catch the instant semantics with traditional methods. Instead of passively waiting for an emergency topic, we try to detect the latent burst topic in its budding stage. In this paper, we propose a fast burst topic detect method, namely, FBTD, which aligns data prediction with characteristic calculation to detect burst term from the real-time spatial-temporal data stream and integrates local topic detection with global topic detection to find the spatial-temporal burst topic. Our method controls the delay within a 0.1 s level while preserving the topic quality. The experiments show that preferable effects are procured, and our method outperforms the state-of-the-art approaches in terms of effectiveness.

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Design for emotion aims to improve human wellbeing. Design for emotion is a multi-disciplinary or interdisciplinary research field, including art, design, computer science, psychology, education, engineering, and other fields. In the last ten years, there has been an increasing focus on design for emotion in various areas. However, there is no comprehensive literature review to set up a design for emotion research framework. To address this gap, this paper first presents a descriptive review of 66 related papers on this topic and then proposes a research framework for emotional design.

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