Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/10957
Title: Can the crowd tell how I feel? Trait empathy and ethnic background in a visual pain judgment task
Authors: Matsangidou, Maria 
Otterbacher, Jahna 
Ang, Chee Siang 
Zaphiris, Panayiotis 
Keywords: Crowdsourcing;Distress;Empathy;Ethnicity;Image metadata;Pain
Category: Electrical Engineering - Electronic Engineering - Information Engineering
Field: Engineering and Technology
Issue Date: 15-Feb-2018
Publisher: Springer Verlag
Source: Universal Access in the Information Society, 2018
DOI: https://doi.org/10.1007/s10209-018-0611-y
Abstract: Many advocate for artificial agents to be empathic. Crowdsourcing could help, by facilitating human-in-the-loop approaches and data set creation for visual emotion recognition algorithms. Although crowdsourcing has been employed successfully for a range of tasks, it is not clear how effective crowdsourcing is when the task involves subjective rating of emotions. We examined relationships between demographics, empathy, and ethnic identity in pain emotion recognition tasks. Amazon MTurkers viewed images of strangers in painful settings, and tagged subjects’ emotions. They rated their level of pain arousal and confidence in their responses, and completed tests to gauge trait empathy and ethnic identity. We found that Caucasian participants were less confident than others, even when viewing other Caucasians in pain. Gender correlated to word choices for describing images, though not to pain arousal or confidence. The results underscore the need for verified information on crowdworkers, to harness diversity effectively for metadata generation tasks.
URI: http://ktisis.cut.ac.cy/handle/10488/10957
ISSN: 16155289
Rights: © The Author(s) 2018
Type: Article
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