TY - GEN
T1 - Uplifing Interviews in Social Science with Individual Data Visualization
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022
AU - Cura, Robin
AU - Beaumont, Amélie
AU - Beuscart, Jean Samuel
AU - Coavoux, Samuel
AU - Latreille De Fozières, Noé
AU - Le Bigot, Brenda
AU - Renisio, Yann
AU - Moussallam, Manuel
AU - Louail, Thomas
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/4/28
Y1 - 2022/4/28
N2 - Collecting accurate and fine-grain information about the music people like, dislike and actually listen to has long been a challenge for sociologists. As millions of people now use online music streaming services, research can build upon the individual listening history data that are collected by these platforms. Individual interviews, in particular, can benefit from such data, by allowing the interviewers to immerse themselves in the musical universe of consenting respondents, and thus ask them contextualized questions and get more precise answers. Designing a visual exploration tool allowing such an immersion is however difficult, because of the volume and heterogeneity of the listening data, the unequal "visual literacy"of the prospective users, or the interviewers' potential lack of knowledge of the music listened to by the respondents. In this case study we discuss the design and evaluation of such a tool. Designed with social scientists, its purpose is to help them in preparing and conducting semi-structured interviews that address various aspects of the listening experience. It was evaluated during thirty interviews with consenting users of a streaming platform in France.
AB - Collecting accurate and fine-grain information about the music people like, dislike and actually listen to has long been a challenge for sociologists. As millions of people now use online music streaming services, research can build upon the individual listening history data that are collected by these platforms. Individual interviews, in particular, can benefit from such data, by allowing the interviewers to immerse themselves in the musical universe of consenting respondents, and thus ask them contextualized questions and get more precise answers. Designing a visual exploration tool allowing such an immersion is however difficult, because of the volume and heterogeneity of the listening data, the unequal "visual literacy"of the prospective users, or the interviewers' potential lack of knowledge of the music listened to by the respondents. In this case study we discuss the design and evaluation of such a tool. Designed with social scientists, its purpose is to help them in preparing and conducting semi-structured interviews that address various aspects of the listening experience. It was evaluated during thirty interviews with consenting users of a streaming platform in France.
KW - computational social science
KW - individual interviews
KW - music streaming data
KW - personal data visualization
U2 - 10.1145/3491101.3503553
DO - 10.1145/3491101.3503553
M3 - Conference contribution
AN - SCOPUS:85129708826
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 30 April 2022 through 5 May 2022
ER -