Lihua Fan
ABSTRACT
In online social sharing of emotions (SSE), individuals use free text to express their emotions and audiences use free text to give affective responses. The role of online SSE is to adjust users' emotions. There is currently a need for a new SSE model that can generate deeper emotional expressions, generate more helpful emotional responses than online SSE, and meet people's need for deep emotional communication. We integrated humanistic drawing-art therapy's thought and method with crowdsourcing to create a guided SSE model. In a crowdsourcing platform, undergraduate students expressed their emotions by drawings, students and counselors crowds used ways of "reflection of feeling" to respond to the drawings. For our questions, we analyzed the response data and designed questionnaires evaluated by all of the participants. Our results revealed: (1) two crowds could generate responses helping painters to understand their emotions; (2) painters could generate deep emotional expression through drawings; and (3) our model could meet people's online deep emotional communication needs.
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